Procesos de conteo, sobredispersión y extensiones

ilustraciones, gráficas, tablas

Autores:: Cifuentes Amado, Maria Victoria

Tipo de recurso:: Doctoral thesis

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

id	UNACIONAL2_7091b746047e3176beac9aaf2493f729
oai_identifier_str	oai:repositorio.unal.edu.co:unal/80388
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Procesos de conteo, sobredispersión y extensiones
dc.title.translated.eng.fl_str_mv	Counting processes, overdispersion and extensions
title	Procesos de conteo, sobredispersión y extensiones
spellingShingle	Procesos de conteo, sobredispersión y extensiones 510 - Matemáticas Regression analysis Scattering (Mathematics) Bayesian statistical decision theory Análisis de regresión Dispersión (Matemáticas) Teoría bayesiana de decisiones estadísticas Datos de conteo Regresión bayesiana Subdispersión Modelos de regresión lineal Regresión no lineal Modelos cero inflados Series de conteo Procesos de Poisson Modelos autoregresivos para series de conteo Sobredispersión Counting data Overdispersion Bayesian regression Underdispersion Linear regression models Nonlinear regression Zero-inflated models Counting time series Poisson processes Autorregresive models
title_short	Procesos de conteo, sobredispersión y extensiones
title_full	Procesos de conteo, sobredispersión y extensiones
title_fullStr	Procesos de conteo, sobredispersión y extensiones
title_full_unstemmed	Procesos de conteo, sobredispersión y extensiones
title_sort	Procesos de conteo, sobredispersión y extensiones
dc.creator.fl_str_mv	Cifuentes Amado, Maria Victoria
dc.contributor.advisor.none.fl_str_mv	Cepeda-Cuervo, Edilberto
dc.contributor.author.none.fl_str_mv	Cifuentes Amado, Maria Victoria
dc.subject.ddc.spa.fl_str_mv	510 - Matemáticas
topic	510 - Matemáticas Regression analysis Scattering (Mathematics) Bayesian statistical decision theory Análisis de regresión Dispersión (Matemáticas) Teoría bayesiana de decisiones estadísticas Datos de conteo Regresión bayesiana Subdispersión Modelos de regresión lineal Regresión no lineal Modelos cero inflados Series de conteo Procesos de Poisson Modelos autoregresivos para series de conteo Sobredispersión Counting data Overdispersion Bayesian regression Underdispersion Linear regression models Nonlinear regression Zero-inflated models Counting time series Poisson processes Autorregresive models
dc.subject.lemb.eng.fl_str_mv	Regression analysis Scattering (Mathematics) Bayesian statistical decision theory
dc.subject.lemb.spa.fl_str_mv	Análisis de regresión Dispersión (Matemáticas)
dc.subject.lemb.sps.fl_str_mv	Teoría bayesiana de decisiones estadísticas
dc.subject.proposal.spa.fl_str_mv	Datos de conteo Regresión bayesiana Subdispersión Modelos de regresión lineal Regresión no lineal Modelos cero inflados Series de conteo Procesos de Poisson Modelos autoregresivos para series de conteo Sobredispersión
dc.subject.proposal.eng.fl_str_mv	Counting data Overdispersion Bayesian regression Underdispersion Linear regression models Nonlinear regression Zero-inflated models Counting time series Poisson processes Autorregresive models
description	ilustraciones, gráficas, tablas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-10-05T16:56:33Z
dc.date.available.none.fl_str_mv	2021-10-05T16:56:33Z
dc.date.issued.none.fl_str_mv	2021-04-19
dc.type.spa.fl_str_mv	Trabajo de grado - Doctorado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/doctoralThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_db06
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TD
format	http://purl.org/coar/resource_type/c_db06
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/80388
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80388 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	[1] J. Achcar, E. Cuervo, and E. Martinez. Use of non-homogeneous poisson processes in the modeling of hospital over admissions in ribeirao preto and region, brazil: An application to respiratory diseases. Journal of Biometrics & Biostatistics, 03, 01 2012. [2] H. Ahn and J. J. Chen. Generation of over-dispersed and under-dispersed binomial variates. Journal of Computational and Graphical Statistics, 4(1):55–64, 1995. [3] M. Al-Osh and A. A. Alzaid. First-order integer-valued autoregressive (inar (1)) process. Journal of Time Series Analysis, 8(3):261–275, 1987. [4] M. Al-Osh and A. A. Alzaid. Integer-valued moving average (inma) process. Statistical Papers, 29(1):281–300, 1988. [5] P. S. Albert, H. F. McFarland, M. E. Smith, and J. A. Frank. Time series for modelling counts from a relapsing-remitting disease: application to modelling disease activity in multiple sclerosis. Statistics in Medicine, 13(5-7):453–466, 1994 [6] A. Alzaid and M. Al-Osh. An integer-valued pth-order autoregressive structure (inar (p)) process. Journal of Applied Probability, pages 314–324, 1990. [7] A. A. Alzaid and M. Al-Osh. Some autoregressive moving average processes with generalized poisson marginal distributions. Annals of the Institute of Statistical Mathematics, 45(2):223–232, 1993. [8] O. Barndorff-Nielsen. Introductory Theory of Exponential Families. John Wiley & Sons, Ltd, 1978. [9] D. Bates and D.Watts. Nonlinear Regression Analysis and Its Applications. Wiley Series in Probability and Statistics. Wiley, 2007. [10] C. Bayes, J. Bazán, and C. Garcia. A new robust regression model for proportions. Bayesian Analysis, 7:841 – 866, 08 2012 [11] N. Bleistein and R. A. Handelsman. Asymptotic expansions of integrals. Dover Books on Mathematics. Dover Publications, New York, 1975. [12] T. Bollerslev. Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3):307–327, 1986. [13] W. Bonat, B. Jørgensen, C. Kokonendji, J. Hinde, and C. Demétrio. Extended poisson tweedie: Properties and regression models for count data. Statistical Modelling: An International Journal, 18, 08 2018. [14] K. Brannas. Explanatory variables in the ar (1) count data model. Umea Economic Studies, 381, 1995. [15] N. Breslow. Extra-Poisson Variation in Log-Linear Models. Appl.Statist., 31:38–44, 1984. [16] P. J. Brockwell, R. A. Davis, and S. E. Fienberg. Time series: theory and methods: theory and methods. Springer Science & Business Media, 1991. [17] R. J. Brooks, W. H. James, and E. Gray. Modelling sub-binomial variation in the frequency of sex combinations in litters of pigs. Biometrics, 47(2):403–417, 1991. [18] S. Brooks and A. Gelman. General methods for monitoring convergence of iterative simulations. J. Comput. Graphi. Stat., 7:434–455, 12 1998. [19] A. Cameron and P. Trivedi. Regression Analysis of Count Data, volume 53. Cambridge University Press, Cambridge, UK, 2013. [20] A. X. Carvalho and M. A. Tanner. Modelling nonlinear count time series with local mixtures of poisson autoregressions. Computational statistics & data analysis, 51(11):5266–5294, 2007. [21] E. Cepeda-Cuervo. Modelagem da variabilidade em modelos lineares generalizados. Unpublished Ph.D. thesis, Mathematics Institute, Universidade Federal do Río de Janeiro, 2001. [22] E. Cepeda-Cuervo and M. V. Cifuentes-Amado. Double generalized beta-binomial and negative binomial regression models. Revista Colombiana de Estadística, 40:141, 01 2017. [23] E. Cepeda-Cuervo and M. V. Cifuentes-Amado. Tilted beta binomial linear regression model: A bayesian approach. 16(1):1–8, 2020. [24] E. Cepeda-Cuervo, M. V. Cifuentes-Amado, and M. Marin. NegBinBetaBinreg: Negative Binomial and Beta Binomial Bayesian Regression Models, 2016. R package version 1.0. [25] E. Cepeda-Cuervo and D. Gamerman. Bayesian methodology for modeling parameters in the two parameter exponential family. Revista Estadística, 57(168-169):93–105, 2005. [26] E. Cepeda-Cuervo, H. S. Migon, L. Garrido, and J. A. Achcar. Generalized linear models with random effects in the two-parameter exponential family. Journal of Statistical Computation and Simulation, 84(3):513–525, 2014 [27] V. Christou and K. Fokianos. Quasi-likelihood inference for negative binomial time series models. Journal of Time Series Analysis, 35(1):55–78, 2014. [28] M. V. Cifuentes-Amado and E. Cepeda-Cuervo. Non-homogeneous poisson process to model seasonal events: Application to the health diseases. International Journal of Statistics in Medical Research, 4(4):337–346, 2015. [29] D. Collet. Modeling Binary Data. Chapman Hall, London, 1991. [30] P. Consul and F. Famoye. Generalized poisson regression model. Communications in Statistics - Theory and Methods, 21(1):89–109, 1992. [31] P. C. Consul and G. C. Jain. A generalization of the poisson distribution. Technometrics, 15(4):791– 799, 1973. [32] R. W. Conway and W. L. Maxwell. A queuing model with state dependent service rates. Journal of Industrial Engineering, 12(2):132–136, 1962. [33] D. R. Cox. Some statistical methods connected with series of events. Journal of the Royal Statistical Society: Series B (Methodological), 17(2):129–157, 1955. [34] D. R. Cox. The statistical analysis of series of events. Monographs on Applied Probability and Statistics, 1966. [35] D. R. Cox. Partial likelihood. Biometrika, 62(2):269–276, 1975. [36] D. R. Cox. Some remarks on overdispersion. Biometrika, 70(1):269–274, 1983. [37] D. R. Cox, G. Gudmundsson, G. Lindgren, L. Bondesson, E. Harsaae, P. Laake, K. Juselius, and S. L. Lauritzen. Statistical analysis of time series: Some recent developments [with discussion and reply]. Scandinavian Journal of Statistics, pages 93–115, 1981. [38] L. H. Crow. Tracking reliability growth. Technical report, ARMY MATERIEL SYSTEMS ANALYSIS ACTIVITY ABERDEEN PROVING GROUND MD, 1974. [39] M. J. Crowder. Beta-binomial anova for proportions. Journal of the Royal Statistical Society. Series C (Applied Statistics), 27(1):34–37, 1978. [40] G. Darmois. Sur les lois de probabilités Ã estimation exhaustive. C. R. Acad. Sci. Paris (in French), (200):1265–1266, 1935. [41] R. A. Davis, W. T. Dunsmuir, and Y. Wang. On autocorrelation in a poisson regression model. Biometrika, 87(3):491–505, 2000. [42] J. del Castillo and M. Pérez-Casany. Weighted poisson distributions for overdispersion and underdispersion situations. Annals of the Institute of Statistical Mathematics, 50:567–585, 02 1998. [43] D. Dey, A. E. Gelfand, and F. Peng. Overdispersed generalized linear models. Journal of Statistical Planning and Inference, 64:93–107, 12 1994. [44] M. L. Diop, A. Diop, and A. Kâ. A negative binomial mixture integer-valued garch model. Afrika Statistika, 13(2):1645–1666, 2018. [45] E. Donoso, J. A. Carvajal, C. Vera, and J. A. Poblete. La edad de la mujer como factor de riesgo de mortalidad materna, fetal, neonatal e infantil. Revista médica de Chile, 142:168 – 174, 02 2014. [46] P. Doukhan, K. Fokianos, and J. Rynkiewicz. Mixtures of nonlinear poisson autoregressions. Journal of Time Series Analysis, 42(1):107–135, 2021. [47] N. R. Draper and H. Smith. Applied regression analysis, volume 326. John Wiley & Sons, 1998. [48] J. Duane. Learning curve approach to reliability monitoring. IEEE transactions on Aerospace, 2(2):563–566, 1964. [49] J. Durbin and S. J. Koopman. Monte carlo maximum likelihood estimation for non-gaussian state space models. Biometrika, 84(3):669–684, 1997. [50] J. Durbin and S. J. Koopman. Time series analysis of non-gaussian observations based on state space models from both classical and bayesian perspectives. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62(1):3–56, 2000. [51] B. Efron. Double exponential families and their use in generalized linear regression. Journal of the American Statistical Association, 81(395):709–721, 1986. [52] A. H. El-Shaarawi, R. Zhu, and H. Joe. Modelling species abundance using the poisson–tweedie family. Environmetrics, 22(2):152–164, 2011. [53] L. J. Emrich and M. R. Piedmonte. A method for generating high-dimensional multivariate binary variates. The American Statistician, 45(4):302–304, 1991. [54] M. J. Faddy. Extended Poisson process modelling and analysis of count data. Biometrical Journal, 39:431 – 440, 01 1997. [55] L. Fahrmeir and H. Kaufmann. Regression models for non-stationary categorical time series. Journal of time series Analysis, 8(2):147–160, 1987. [56] W. Feller. Xii. In An introduction to probability theory and its applications, volume 1. Wiley, 1950. [57] R. Ferland, A. Latour, and D. Oraichi. Integer-valued garch process. Journal of Time Series Analysis, 27(6):923–942, 2006. [58] R. A. Fisher. The significance of deviations from expectation in a poisson series. Biometrics, 6(1):17– 24, 1950. [59] J. M. Flegal. Monte Carlo standard errors for Markov chain Monte Carlo. PhD thesis, 2008. [60] K. Fokianos. Truncated poisson regression for time series of counts. Scandinavian journal of statistics, 28(4):645–659, 2001. [61] K. Fokianos. Count time series models. In Handbook of statistics, volume 30, pages 315–347. Elsevier, 2012. [62] K. Fokianos and B. Kedem. Partial likelihood inference for time series following generalized linear models. Journal of Time Series Analysis, 25(2):173–197, 2004. [63] K. Fokianos, A. Rahbek, and D. Tjøstheim. Poisson autoregression. Journal of the American Statistical Association, 104(488):1430–1439, 2009. [64] K. Fokianos and D. Tjøstheim. Log-linear poisson autoregression. Journal of Multivariate Analysis, 102(3):563–578, 2011. [65] K. Fokianos and D. Tjøstheim. Nonlinear poisson autoregression. Annals of the Institute of Statistical Mathematics, 64(6):1205–1225, 2012. [66] G. A. Fox, G. A. Negrete-Yankelevich, and V. J. Sosa. Ecological Statistics: Contemporary Theory and Application. Oxford : Oxford University Press, 2015. [67] E. Frome and R. DuFrain. Maximum likelihood estimation for cytogenetic dose-response curves. Biometrics, pages 73–84, 1986. [68] E. L. Frome. The analysis of rates using poisson regression models. Biometrics, pages 665–674, 1983. [69] E. L. Frome, M. H. Kutner, and J. J. Beauchamp. Regression analysis of poisson-distributed data. Journal of the American Statistical Association, 68(344):935–940, 1973. [70] D. Gamerman. Sampling from the posterior distribution in generalized linear mixed models. Statistics and Computing, 7(1):57–68, Mar 1997. [71] D. Gamerman, T. R. dos Santos, and G. C. Franco. A non-gaussian family of state-space models with exact marginal likelihood. Journal of Time Series Analysis, 34(6):625–645, 2013. [72] A. E. Gelfand and S. R. Dalal. A note on overdispersed exponential families. Biometrika, 77(1):55–64,1990. [73] J. Geweke. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In J. M. Bernardo, J. Berger, A. P. Dawid, and J. F. M. Smith, editors, Bayesian Statistics 4, pages 169–193. Oxford University Press, Oxford, 1992. [74] S. K. Ghosh, P. Mukhopadhyay, and J.-C. Lu. Bayesian analysis of zero-inflated regression models. Journal of Statistical Planning and Inference, 136(4):1360–1375, 2006 [75] A. L. Goel. Software reliability modelling and estimation techniques. Technical report, SYRACUSE UNIV NY DEPT OF INDUSTRIAL ENGINEERING AND OPERATIONS RESEARCH, 1982. [76] M. Greenwood and G. U. Yule. An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal statistical society, 83(2):255–279, 1920. [77] S. Gurmu. Semiparametric estimation of hurdle regression models with an application to medicaid utilization. Journal of Applied Econometrics, 12:225–242, 05 1997. [78] S. Gurmu and P. K. Trivedi. Excess zeros in count models for recreational trips. Journal of Business & Economic Statistics, 14(4):469–477, 1996. [79] E. Hahn. Mixture densities for project management activity times: A robust approach to pert. European Journal of Operational Research, 188:450–459, 02 2008. [80] E. Hahn and M. López Martín. Robust project management with the tilted beta distribution. SORT, 39:253–272, 01 2015. [81] D. B. Hall. Zero-inflated poisson and binomial regression with random effects: A case study. Biometrics, 56(4):1030–1039, 2000. [82] W. K. Hastings. Monte carlo sampling methods using markov chains and their applications. 1970. [83] D. C. Heilbron. Generalized linear models for altered zero probabilities and overdispersion in count data. SIMS Technical Report 9, Department of Epidemiology and Biostatistics, University of California, San Francisco, 1989. [84] D. C. Heilbron. Zero-altered and other regression models for count data with added zeros. Biometrical Journal, 36(5):531–547, 1994. [85] J. Hinde and C. Demétrio. Overdispersion: Models and estimation. Computational Statistics & Data Analysis, 27:151–170, 02 1998. [86] P. Hougaard, M.-L. T. Lee, and G. Whitmore. Analysis of overdispersed count data by mixtures of poisson variables and poisson processes. Biometrics, pages 1225–1238, 1997. [87] Instituto Nacional de Estadísticas, Servicio de Registro Civil e Identificación, Departamento de Estadísticas e Información de Salud, Consultado el: 2019-04-10. [88] M. J. Crowder. Beta-binomial anova for proportions. Applied Statistics, 27:34, 01 1978. [89] P. A. Jacobs and P. A. Lewis. Discrete time series generated by mixtures. i: Correlational and runs properties. Journal of the Royal Statistical Society: Series B (Methodological), 40(1):94–105, 1978. [90] H. Joe and R. Zhu. Generalized poisson distribution: the property of mixture of poisson and comparison with negative binomial distribution. Biometrical Journal: Journal of Mathematical Methods in Biosciences, 47(2):219–229, 2005 [91] B. Jorgensen. Exponential dispersion models. Journal of the Royal Statistical Society. Series B (Methodological), 49(2):127–162, 1987. [92] B. Jorgensen. The theory of dispersion models. CRC Press, 1997. [93] B. Jørgensen and C. Kokonendji. Discrete dispersion models and their tweedie asymptotics. AStA Advances in Statistical Analysis, 100, 09 2014. [94] B. Kedem and K. Fokianos. Regression models for time series analysis, volume 488. John Wiley & Sons, 2005. [95] R. Kenett. ’the com-poisson model for count data: A survey of methods and applications’ by k. sellers, s. borle and g. shmueli. Applied Stochastic Models in Business and Industry, 28:117–121, 03 2012 [96] J. F. C. Kingman. Poisson processes, volume 3 of Oxford Studies in Probability. The Clarendon Press Oxford University Press, New York, 1993. Oxford Science Publications. [97] C. C. Kokonendji. Over-and underdispersion models. Methods and applications of statistics in clinical Trials: Planning, analysis, and inferential methods, 2:506–526, 2014. [98] C. C. Kokonendji, C. G. Demétrio, and S. S. Zocchi. On hinde–demétrio regression models for overdispersed count data. Statistical Methodology, 4(3):277–291, 2007. [99] B. O. Koopman. On distributions admitting a sufficient statistic. Transactions of the American Mathematical Society, 39(3):399–409, 1936. [100] L. Kuo and T. Yang. Bayesian computation for nonhomogeneous poisson process in software reliability. Journal of the American Statistical Association, 91:763–773, 1996. [101] D. Lambert. Zero-inflated poisson regression, with an application to defects in manufacturing. Technometrics, 34:1–14, 02 1992. [102] A. Latour. Existence and stochastic structure of a non-negative integer-valued autoregressive process. Journal of Time Series Analysis, 19(4):439–455, 1998. [103] J. F. Lawless. Negative binomial and mixed poisson regression. The Canadian Journal of Statistics/La Revue Canadienne de Statistique, pages 209–225, 1987. [104] B. Li, P. McCullagh, et al. Potential functions and conservative estimating functions. The Annals of Statistics, 22(1):340–356, 1994. [105] Q. Li, H. Lian, and F. Zhu. Robust closed-form estimators for the integer-valued garch (1, 1) model. Computational Statistics & Data Analysis, 101:209–225, 2016. [106] L. Lin. Quasi bayesian likelihood. Statistical Methodology, 3(4):444–455, 2006. [107] D. V. Lindley. Fiducial distributions and bayes’ theorem. Journal of the Royal Statistical Society. Series B (Methodological), pages 102–107, 1958. [108] J. Lindsey. Modelling Frequency and Count Data. Oxford University Press, Oxford, 1995. [109] D. Lord, S. D. Guikema, et al. The conway–maxwell–poisson model for analyzing crash data. Applied Stochastic Models in Business and Industry, 28(2):122–127, 2012. [110] I. L. MacDonald and W. Zucchini. Hidden Markov and other models for discrete-valued time series, volume 110. CRC Press, 1997. [111] H. Mao, F. Zhu, and Y. Cui. A generalized mixture integer-valued garch model. Statistical Methods & Applications, 29(3):527–552, 2020. [112] B. H. Margolin, N. Kaplan, and E. Zeiger. Statistical analysis of the ames salmonella/microsome test. Proceedings of the National Academy of Sciences, 78(6):3779–3783, 1981. [113] B. H. Margolin, N. Kaplan, and E. Zeiger. Statistical analysis of the ames salmonella/microsome test. Proceedings of the National Academy of Sciences, 78(6):3779–3783, 1981. [114] C. Mcalpine, J. R. Rhodes, M. E. Bowen, D. Lunney, J. G. Callaghan, D. Mitchell, and H. Possingham. Can multiscale models of species distribution be generalized from region to region? a case study of the koala. Journal of Applied Ecology, 45, 04 2008. [115] P. McCullagh. Quasi-likelihood functions. The Annals of Statistics, pages 59–67, 1983. [116] P. McCullagh and J. Nelder. Generalized Linear Models, Second Edition. Chapman and Hall/CRC Monographs on Statistics and Applied Probability Series. Chapman & Hall, 1989. [117] E. McKenzie. Autoregressive moving-average processes with negative-binomial and geometric marginal distributions. Advances in Applied Probability, pages 679–705, 1986. [118] N. Metropolis and S. Ulam. The monte carlo method. Journal of the American statistical association, 44(247):335–341, 1949. [119] S.-P. Miaou. The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accident Analysis & Prevention, 26(4):471–482, 1994. [120] J. Mullahy. Specification and testing of some modified count data models. Journal of Econometrics, 33(3):341 – 365, 1986. [121] J. Mullahy. Heterogeneity, excess zeros, and the structure of count data models. Journal of Applied Econometrics, 12:337–50, 05 1997. [122] J. D. Musa and K. Okumoto. A logarithmic poisson execution time model for software reliability measurement. In Proceedings of the 7th international conference on Software engineering, pages 230– 238. Citeseer, 1984. [123] S. Nakajima, K. Watanabe, and M. Sugiyama. Variational Bayesian learning theory. Cambridge University Press, 2019. [124] B. H. Neelon, A. J. O’Malley, and S.-L. T. Normand. A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use. Statistical Modelling, 10(4):421–439, 2010. [125] J. A. Nelder and D. Pregibon. An extended quasi-likelihood function. Biometrika, 74(2):221–232, 1987. [126] J. A. Nelder and R. W. Wedderburn. Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3):370–384, 1972. [127] A. Noack. A class of random variables with discrete distributions. Ann. Math. Statist., 21(1):127–132, 03 1950. [128] K. Okumoto and A. L. Goel. Optimum release time for software systems based on reliability and cost criteria. Journal of Systems and Software, 1:315–318, 1979. [129] F. W. Olver, D. W. Lozier, R. F. Boisvert, and C. W. Clark. Nist handbook of mathematical functions hardback and cd-rom. chapter 8, pages 173–192. Cambridge university press, 2010. [130] G. Patil. Certain properties of the generalized power series distribution. Annals Inst. Statist. Math., Tokyo, 14:179–182, 01 1962. [131] G. P. Patil. Maximum likelihood estimation for generalized power series distributions and its application to a truncated binomial distribution. Biometrika, 49(1/2):227–237, 1962. [132] K. Pearson. Tables of the incomplete beta function. Technical report, 1934. [133] R. Petterle, W. Bonat, C. Kokonendji, J. Seganfredo, A. Moraes, and M. Silva. Double poisson-tweedie regression models. The International Journal of Biostatistics, 15, 04 2019. [134] A. Pievatolo and F. Ruggeri. Bayesian reliability analysis of complex repairable systems. Applied Stochastic Models in Business and Industry, 20:253–264, 2004. [135] E. J. G. Pitman. Sufficient statistics and intrinsic accuracy. Mathematical Proceedings of the Cambridge Philosophical Society, 32(4):567–579, 1936. [136] J. M. Potts and J. Elith. Comparing species abundance models. Ecological Modelling, 199(2):153–163, 2006. [137] M. B. Priestley. Spectral analysis and time series: probability and mathematical statistics. Number 04; QA280, P7. 1981. [138] P. Puig and J. Valero. Count data distributions: some characterizations with applications. Journal of the American Statistical Association, 101(473):332–340, 2006. [139] M. Pujol, J.-F. Barquinero, P. Puig, R. Puig, M. R. Caballín, and L. Barrios. A new model of biodosimetry to integrate low and high doses. PLoS One, 9(12):e114137, 2014. [140] S. Quine et al. Achievement orientation of aboriginal and white australian adolescents. 1973. [141] A. Quintero-Sarmiento, E. Cepeda-Cuervo, and V. Núñez Antón. Estimating infant mortality in colombia: Some overdispersion modelling approaches. Journal of Applied Statistics, 39:1011–1036, 05 2012. [142] J. Ramirez-Cid and J. Achcar. Bayesian inference for non-homogeneous poisson process in software reliability models. Computational Statistics and Data Analysis, 32:147–159, 1999. [143] J. E. Ramírez-Cid and J. A. Achcar. Bayesian inference for nonhomogeneous poisson processes in software reliability models assuming nonmonotonic intensity functions. Computational statistics & data analysis, 32(2):147–159, 1999. [144] M. Ridout, C. Demétrio, and J. Hinle. Models for count data with many zeros. international biomeric conference. Cape Town, 13:1–13, 01 1998. [145] S. Ross, J. Kelly, K. M. R. Collection, R. Sullivan, W. Perry, D. Mercer, R. Davis, T. Washburn, V. Sager, J. Boyce, et al. Stochastic Processes. Number v. 1 in Probability and Statistics Series. Wiley, 1983. [146] A. Saei, J. Ward, and C. A McGilchrist. Threshold models in a methadone programme evaluation. Statistics in medicine, 15:2253–60, 10 1996. [147] M. Sankaran. 275. note: The discrete poisson-lindley distribution. Biometrics, 26(1):145–149, 1970. [148] G. Seber and C. Wild. Nonlinear Regression. Wiley Series in Probability and Statistics. Wiley, 2005. [149] G. Shmueli, T. Minka, J. Kadane, S. Borle, and P. Boatwright. A useful distribution for fitting discrete data: Revival of the conway-maxwell-poisson distribution. Journal of the Royal Statistical Society Series C, 54:127–142, 02 2005. [150] R. H. Shumway and D. S. Stoffer. Time series analysis and its applications. Studies In Informatics And Control, 9(4):375–376, 2000. [151] A. Singh and G. Roberts. State space modelling of cross-classified time series of counts. International Statistical Review/Revue Internationale de Statistique, pages 321–335, 1992. [152] D. Spiegelhalter, A. Thomas, N. Best, and D. Lunn. WinBUGS user manual. MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK, 2003 [153] D. Spiegelhalter, A. Thomas, N. Best, D. Lunn, and K. Rice. OpenBUGS examples, volume i. 2014. [154] F. W. Steutel and K. van Harn. Discrete analogues of self-decomposability and stability. The Annals of Probability, pages 893–899, 1979. [155] M. A. Tanner and W. Hung Wong. The calculation of posterior distributions by data augmentation: Rejoinder. Journal of The American Statistical Association, 82:528–540, 06 1987. [156] N. M. Temme. Special functions: An introduction to the classical functions of mathematical physics. John Wiley & Sons, 2011. [157] Zero-Inflated Poisson Regression and R Data Analysis Examples @ONLINE, Consultado el: 2019-03-27. [158] L. von Bortkiewicz and V. I. Bortkevič. Das gesetz der kleinen zahlen. BG Teubner, 1898. [159] R. W. M. Wedderburn. Quasi-likelihood functions, generalized linear models, and the gauss-newton method. Biometrika, 61(3):439–447, 1974. [160] C. H.Weiß. The inarch (1) model for overdispersed time series of counts. Communications in Statistics- Simulation and Computation, 39(6):1269–1291, 2010. [161] C. H. Weiß. Integer-valued autoregressive models for counts showing underdispersion. Journal of Applied Statistics, 40(9):1931–1948, 2013. [162] A.Welsh, R. Cunningham, C. Donnelly, and D. Lindenmayer. Modelling the abundance of rare species: statistical models for counts with extra zeros. Ecological Modelling, 88(1):297–308, 1996. [163] M.West, P. J. Harrison, and H. S. Migon. Dynamic generalized linear models and bayesian forecasting. Journal of the American Statistical Association, 80(389):73–83, 1985. [164] D. Williams. Extra-binomial variation in logistic linear models. Appl.Statist., 2:144–188, 1982. [165] R. Winkelmann. Duration dependence and dispersion in count-data models. Journal of Business & Economic Statistics, 13(4):467–474, 1995. [166] E. Xekalaki. Under-and overdispersion. Wiley StatsRef: Statistics Reference Online, 2014. [167] M. Xie and G. Hong. Ch. 28. software reliability modeling, estimation and analysis. In Advances in Reliability, volume 20 of Handbook of Statistics, pages 707 – 731. Elsevier, 2001. [168] T. Y. Yang and L. Kuo. Bayesian computation for the superposition of nonhomogeneous poisson processes. Canadian Journal of Statistics, 27(3):547–556, 1999. [169] S. L. Zeger. A regression model for time series of counts. Biometrika, 75(4):621–629, 1988. [170] S. L. Zeger and B. Qaqish. Markov regression models for time series: a quasi-likelihood approach. Biometrics, pages 1019–1031, 1988. [171] F. Zhu. A negative binomial integer-valued garch model. Journal of Time Series Analysis, 32(1):54–67, 2011. [172] F. Zhu. Modeling overdispersed or underdispersed count data with generalized poisson integer-valued garch models. Journal of Mathematical Analysis and Applications, 389(1):58–71, 2012. [173] F. Zhu, Q. Li, and D. Wang. A mixture integer-valued arch model. Journal of Statistical Planning and inference, 140(7):2025–2036, 2010. [174] F. Zhu, L. Shi, and S. Liu. Influence diagnostics in log-linear integer-valued garch models. AStA Advances in Statistical Analysis, 99(3):311–335, 2015. [175] A. Zuur, E. Ieno, N. Walker, A. Saveliev, and G. Smith. Mixed Effects Models and Extensions in Ecology with R. Statistics for Biology and Health. Springer New York, 2009.
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	ix, 151 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias - Doctorado en Ciencias - Matemáticas
dc.publisher.department.spa.fl_str_mv	Departamento de Matemáticas
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/80388/1/license.txt https://repositorio.unal.edu.co/bitstream/unal/80388/2/1013597805.2021.pdf https://repositorio.unal.edu.co/bitstream/unal/80388/3/1013597805.2021.pdf.jpg
bitstream.checksum.fl_str_mv	cccfe52f796b7c63423298c2d3365fc6 60db03f5d66fcc94f313375fd6d1a63a d8c0ba9ca80ed4dad5d1139bbd94fd4c
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1814089824312754176
spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cepeda-Cuervo, Edilberto5d0ed32887a693c303b0ad910550b643600Cifuentes Amado, Maria Victoria51f3432c57bac34329bedc4d01d7b4912021-10-05T16:56:33Z2021-10-05T16:56:33Z2021-04-19https://repositorio.unal.edu.co/handle/unal/80388Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasSe presenta una revisión detallada de modelos sobredispersos de regresión lineal y no lineal para datos de conteo, desde un enfoque Bayesiano. Se propone la función de distribución beta inclinada binomial, se estudian sus propiedades y se proponen los modelos de regresión lineal, donde se asignan estructuras de regresión a la media, parámetro de dispersión y parámetro de mixtura. Adicionalmente, se presentan las reparametrizaciones de las distribuciones beta binomial y binomial negativa, en términos de la media, y se establecen los modelos de regresión Bayesiana, proponiendo nuevas variables de trabajo para el parámetro de dispersión, que reducen la autocorrelación y mejoran la convergencia de las cadenas. Se define una extensión a modelos de regresión no lineal y se propone el algoritmo Bayesiano para este caso. Se definen nuevos modelos de regresión no lineal con exceso de ceros y se extiende la metodología Bayesiana propuesta en [21] para estos modelos: se desarrolla el algoritmo de Metropolis-Hastings y se proponen las variables de trabajo requeridas, a partir del método de aumento de datos de [74]. Se proponen modelos subdispersos no lineales doblemente generalizados para datos de conteo que presentan subdispersión con respecto a las distribuciones Poisson o binomial, y se definen funciones de cuasi-verosimilitud adecuadas, a partir del enfoque de [106], para el ajuste de los modelos Bayesianos. Finalmente, se proponen nuevos procesos de Poisson no homogéneos cíclicos y se definen modelos autoregresivos no lineales doblemente generalizados para series de conteo, basados en distribuciones de conteo subdispersas y se aplica el paradigma Bayesiano para la estimación. (Texto tomado de la fuente).A review of overdispersed linear and nonlinear regression models for counting data is presented, from a Bayesian approach. The tilted beta binomial distribution function is proposed, its properties are studied and the linear regression models are proposed, where regression structures are assigned to the mean, parameter of dispersion and mixture parameter. In addition, the reparametrizations of the beta binomial and negative binomial distributions are presented, in terms of the mean, and the Bayesian regression models are inroduced, by proposing new suitable working variables for the dispersion parameter, which reduce autocorrelation and improve chain convergence. An extension to non-linear regression models is defined and the Bayesian algorithm is proposed for this case. New nonlinear regression models zero-inflated are defined and the Bayesian methodology, proposed by [21], is extended for these models: the Metropolis-Hastings algorithm is developed and the requiered working variables are proposed, from the data augmentation method of [74]. Double generalized nonlinear sub-dispersed models are proposed for counting data that present subdispersion with respect to the Poisson or binomial distributions, and appropriate quasi-likelihood functions are defined, from the [106] approach, which are useful for the Bayesian regression in these models. New cyclic non-homogeneous Poisson processes are proposed and doubly generalized nonlinear autoregressive models are defined for count series, based on sub-dispersed count distributions and Bayesian paradigm is applied for the estimation.DoctoradoDoctor en Ciencias - Matemáticasix, 151 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Doctorado en Ciencias - MatemáticasDepartamento de MatemáticasFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - MatemáticasRegression analysisScattering (Mathematics)Bayesian statistical decision theoryAnálisis de regresiónDispersión (Matemáticas)Teoría bayesiana de decisiones estadísticasDatos de conteoRegresión bayesianaSubdispersiónModelos de regresión linealRegresión no linealModelos cero infladosSeries de conteoProcesos de PoissonModelos autoregresivos para series de conteoSobredispersiónCounting dataOverdispersionBayesian regressionUnderdispersionLinear regression modelsNonlinear regressionZero-inflated modelsCounting time seriesPoisson processesAutorregresive modelsProcesos de conteo, sobredispersión y extensionesCounting processes, overdispersion and extensionsTrabajo de grado - Doctoradoinfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_db06Texthttp://purl.org/redcol/resource_type/TD[1] J. Achcar, E. Cuervo, and E. Martinez. Use of non-homogeneous poisson processes in the modeling of hospital over admissions in ribeirao preto and region, brazil: An application to respiratory diseases. Journal of Biometrics & Biostatistics, 03, 01 2012.[2] H. Ahn and J. J. Chen. Generation of over-dispersed and under-dispersed binomial variates. Journal of Computational and Graphical Statistics, 4(1):55–64, 1995.[3] M. Al-Osh and A. A. Alzaid. First-order integer-valued autoregressive (inar (1)) process. Journal of Time Series Analysis, 8(3):261–275, 1987.[4] M. Al-Osh and A. A. Alzaid. Integer-valued moving average (inma) process. Statistical Papers, 29(1):281–300, 1988.[5] P. S. Albert, H. F. McFarland, M. E. Smith, and J. A. Frank. Time series for modelling counts from a relapsing-remitting disease: application to modelling disease activity in multiple sclerosis. Statistics in Medicine, 13(5-7):453–466, 1994[6] A. Alzaid and M. Al-Osh. An integer-valued pth-order autoregressive structure (inar (p)) process. Journal of Applied Probability, pages 314–324, 1990.[7] A. A. Alzaid and M. Al-Osh. Some autoregressive moving average processes with generalized poisson marginal distributions. Annals of the Institute of Statistical Mathematics, 45(2):223–232, 1993.[8] O. Barndorff-Nielsen. Introductory Theory of Exponential Families. John Wiley & Sons, Ltd, 1978.[9] D. Bates and D.Watts. Nonlinear Regression Analysis and Its Applications. Wiley Series in Probability and Statistics. Wiley, 2007.[10] C. Bayes, J. Bazán, and C. Garcia. A new robust regression model for proportions. Bayesian Analysis, 7:841 – 866, 08 2012[11] N. Bleistein and R. A. Handelsman. Asymptotic expansions of integrals. Dover Books on Mathematics. Dover Publications, New York, 1975.[12] T. Bollerslev. Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3):307–327, 1986.[13] W. Bonat, B. Jørgensen, C. Kokonendji, J. Hinde, and C. Demétrio. Extended poisson tweedie: Properties and regression models for count data. Statistical Modelling: An International Journal, 18, 08 2018.[14] K. Brannas. Explanatory variables in the ar (1) count data model. Umea Economic Studies, 381, 1995.[15] N. Breslow. Extra-Poisson Variation in Log-Linear Models. Appl.Statist., 31:38–44, 1984.[16] P. J. Brockwell, R. A. Davis, and S. E. Fienberg. Time series: theory and methods: theory and methods. Springer Science & Business Media, 1991.[17] R. J. Brooks, W. H. James, and E. Gray. Modelling sub-binomial variation in the frequency of sex combinations in litters of pigs. Biometrics, 47(2):403–417, 1991.[18] S. Brooks and A. Gelman. General methods for monitoring convergence of iterative simulations. J. Comput. Graphi. Stat., 7:434–455, 12 1998.[19] A. Cameron and P. Trivedi. Regression Analysis of Count Data, volume 53. Cambridge University Press, Cambridge, UK, 2013.[20] A. X. Carvalho and M. A. Tanner. Modelling nonlinear count time series with local mixtures of poisson autoregressions. Computational statistics & data analysis, 51(11):5266–5294, 2007.[21] E. Cepeda-Cuervo. Modelagem da variabilidade em modelos lineares generalizados. Unpublished Ph.D. thesis, Mathematics Institute, Universidade Federal do Río de Janeiro, 2001.[22] E. Cepeda-Cuervo and M. V. Cifuentes-Amado. Double generalized beta-binomial and negative binomial regression models. Revista Colombiana de Estadística, 40:141, 01 2017.[23] E. Cepeda-Cuervo and M. V. Cifuentes-Amado. Tilted beta binomial linear regression model: A bayesian approach. 16(1):1–8, 2020.[24] E. Cepeda-Cuervo, M. V. Cifuentes-Amado, and M. Marin. NegBinBetaBinreg: Negative Binomial and Beta Binomial Bayesian Regression Models, 2016. R package version 1.0.[25] E. Cepeda-Cuervo and D. Gamerman. Bayesian methodology for modeling parameters in the two parameter exponential family. Revista Estadística, 57(168-169):93–105, 2005.[26] E. Cepeda-Cuervo, H. S. Migon, L. Garrido, and J. A. Achcar. Generalized linear models with random effects in the two-parameter exponential family. Journal of Statistical Computation and Simulation, 84(3):513–525, 2014[27] V. Christou and K. Fokianos. Quasi-likelihood inference for negative binomial time series models. Journal of Time Series Analysis, 35(1):55–78, 2014.[28] M. V. Cifuentes-Amado and E. Cepeda-Cuervo. Non-homogeneous poisson process to model seasonal events: Application to the health diseases. International Journal of Statistics in Medical Research, 4(4):337–346, 2015.[29] D. Collet. Modeling Binary Data. Chapman Hall, London, 1991.[30] P. Consul and F. Famoye. Generalized poisson regression model. Communications in Statistics - Theory and Methods, 21(1):89–109, 1992.[31] P. C. Consul and G. C. Jain. A generalization of the poisson distribution. Technometrics, 15(4):791– 799, 1973.[32] R. W. Conway and W. L. Maxwell. A queuing model with state dependent service rates. Journal of Industrial Engineering, 12(2):132–136, 1962.[33] D. R. Cox. Some statistical methods connected with series of events. Journal of the Royal Statistical Society: Series B (Methodological), 17(2):129–157, 1955.[34] D. R. Cox. The statistical analysis of series of events. Monographs on Applied Probability and Statistics, 1966.[35] D. R. Cox. Partial likelihood. Biometrika, 62(2):269–276, 1975.[36] D. R. Cox. Some remarks on overdispersion. Biometrika, 70(1):269–274, 1983.[37] D. R. Cox, G. Gudmundsson, G. Lindgren, L. Bondesson, E. Harsaae, P. Laake, K. Juselius, and S. L. Lauritzen. Statistical analysis of time series: Some recent developments [with discussion and reply]. Scandinavian Journal of Statistics, pages 93–115, 1981.[38] L. H. Crow. Tracking reliability growth. Technical report, ARMY MATERIEL SYSTEMS ANALYSIS ACTIVITY ABERDEEN PROVING GROUND MD, 1974.[39] M. J. Crowder. Beta-binomial anova for proportions. Journal of the Royal Statistical Society. Series C (Applied Statistics), 27(1):34–37, 1978.[40] G. Darmois. Sur les lois de probabilités Ã estimation exhaustive. C. R. Acad. Sci. Paris (in French), (200):1265–1266, 1935.[41] R. A. Davis, W. T. Dunsmuir, and Y. Wang. On autocorrelation in a poisson regression model. Biometrika, 87(3):491–505, 2000.[42] J. del Castillo and M. Pérez-Casany. Weighted poisson distributions for overdispersion and underdispersion situations. Annals of the Institute of Statistical Mathematics, 50:567–585, 02 1998.[43] D. Dey, A. E. Gelfand, and F. Peng. Overdispersed generalized linear models. Journal of Statistical Planning and Inference, 64:93–107, 12 1994.[44] M. L. Diop, A. Diop, and A. Kâ. A negative binomial mixture integer-valued garch model. Afrika Statistika, 13(2):1645–1666, 2018.[45] E. Donoso, J. A. Carvajal, C. Vera, and J. A. Poblete. La edad de la mujer como factor de riesgo de mortalidad materna, fetal, neonatal e infantil. Revista médica de Chile, 142:168 – 174, 02 2014.[46] P. Doukhan, K. Fokianos, and J. Rynkiewicz. Mixtures of nonlinear poisson autoregressions. Journal of Time Series Analysis, 42(1):107–135, 2021.[47] N. R. Draper and H. Smith. Applied regression analysis, volume 326. John Wiley & Sons, 1998.[48] J. Duane. Learning curve approach to reliability monitoring. IEEE transactions on Aerospace, 2(2):563–566, 1964.[49] J. Durbin and S. J. Koopman. Monte carlo maximum likelihood estimation for non-gaussian state space models. Biometrika, 84(3):669–684, 1997.[50] J. Durbin and S. J. Koopman. Time series analysis of non-gaussian observations based on state space models from both classical and bayesian perspectives. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62(1):3–56, 2000.[51] B. Efron. Double exponential families and their use in generalized linear regression. Journal of the American Statistical Association, 81(395):709–721, 1986.[52] A. H. El-Shaarawi, R. Zhu, and H. Joe. Modelling species abundance using the poisson–tweedie family. Environmetrics, 22(2):152–164, 2011.[53] L. J. Emrich and M. R. Piedmonte. A method for generating high-dimensional multivariate binary variates. The American Statistician, 45(4):302–304, 1991.[54] M. J. Faddy. Extended Poisson process modelling and analysis of count data. Biometrical Journal, 39:431 – 440, 01 1997.[55] L. Fahrmeir and H. Kaufmann. Regression models for non-stationary categorical time series. Journal of time series Analysis, 8(2):147–160, 1987.[56] W. Feller. Xii. In An introduction to probability theory and its applications, volume 1. Wiley, 1950.[57] R. Ferland, A. Latour, and D. Oraichi. Integer-valued garch process. Journal of Time Series Analysis, 27(6):923–942, 2006.[58] R. A. Fisher. The significance of deviations from expectation in a poisson series. Biometrics, 6(1):17– 24, 1950.[59] J. M. Flegal. Monte Carlo standard errors for Markov chain Monte Carlo. PhD thesis, 2008.[60] K. Fokianos. Truncated poisson regression for time series of counts. Scandinavian journal of statistics, 28(4):645–659, 2001.[61] K. Fokianos. Count time series models. In Handbook of statistics, volume 30, pages 315–347. Elsevier, 2012.[62] K. Fokianos and B. Kedem. Partial likelihood inference for time series following generalized linear models. Journal of Time Series Analysis, 25(2):173–197, 2004.[63] K. Fokianos, A. Rahbek, and D. Tjøstheim. Poisson autoregression. Journal of the American Statistical Association, 104(488):1430–1439, 2009.[64] K. Fokianos and D. Tjøstheim. Log-linear poisson autoregression. Journal of Multivariate Analysis, 102(3):563–578, 2011.[65] K. Fokianos and D. Tjøstheim. Nonlinear poisson autoregression. Annals of the Institute of Statistical Mathematics, 64(6):1205–1225, 2012.[66] G. A. Fox, G. A. Negrete-Yankelevich, and V. J. Sosa. Ecological Statistics: Contemporary Theory and Application. Oxford : Oxford University Press, 2015.[67] E. Frome and R. DuFrain. Maximum likelihood estimation for cytogenetic dose-response curves. Biometrics, pages 73–84, 1986.[68] E. L. Frome. The analysis of rates using poisson regression models. Biometrics, pages 665–674, 1983.[69] E. L. Frome, M. H. Kutner, and J. J. Beauchamp. Regression analysis of poisson-distributed data. Journal of the American Statistical Association, 68(344):935–940, 1973.[70] D. Gamerman. Sampling from the posterior distribution in generalized linear mixed models. Statistics and Computing, 7(1):57–68, Mar 1997.[71] D. Gamerman, T. R. dos Santos, and G. C. Franco. A non-gaussian family of state-space models with exact marginal likelihood. Journal of Time Series Analysis, 34(6):625–645, 2013.[72] A. E. Gelfand and S. R. Dalal. A note on overdispersed exponential families. Biometrika, 77(1):55–64,1990.[73] J. Geweke. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In J. M. Bernardo, J. Berger, A. P. Dawid, and J. F. M. Smith, editors, Bayesian Statistics 4, pages 169–193. Oxford University Press, Oxford, 1992.[74] S. K. Ghosh, P. Mukhopadhyay, and J.-C. Lu. Bayesian analysis of zero-inflated regression models. Journal of Statistical Planning and Inference, 136(4):1360–1375, 2006[75] A. L. Goel. Software reliability modelling and estimation techniques. Technical report, SYRACUSE UNIV NY DEPT OF INDUSTRIAL ENGINEERING AND OPERATIONS RESEARCH, 1982.[76] M. Greenwood and G. U. Yule. An inquiry into the nature of frequency distributions representative of multiple happenings with particular reference to the occurrence of multiple attacks of disease or of repeated accidents. Journal of the Royal statistical society, 83(2):255–279, 1920.[77] S. Gurmu. Semiparametric estimation of hurdle regression models with an application to medicaid utilization. Journal of Applied Econometrics, 12:225–242, 05 1997.[78] S. Gurmu and P. K. Trivedi. Excess zeros in count models for recreational trips. Journal of Business & Economic Statistics, 14(4):469–477, 1996.[79] E. Hahn. Mixture densities for project management activity times: A robust approach to pert. European Journal of Operational Research, 188:450–459, 02 2008.[80] E. Hahn and M. López Martín. Robust project management with the tilted beta distribution. SORT, 39:253–272, 01 2015.[81] D. B. Hall. Zero-inflated poisson and binomial regression with random effects: A case study. Biometrics, 56(4):1030–1039, 2000.[82] W. K. Hastings. Monte carlo sampling methods using markov chains and their applications. 1970.[83] D. C. Heilbron. Generalized linear models for altered zero probabilities and overdispersion in count data. SIMS Technical Report 9, Department of Epidemiology and Biostatistics, University of California, San Francisco, 1989.[84] D. C. Heilbron. Zero-altered and other regression models for count data with added zeros. Biometrical Journal, 36(5):531–547, 1994.[85] J. Hinde and C. Demétrio. Overdispersion: Models and estimation. Computational Statistics & Data Analysis, 27:151–170, 02 1998.[86] P. Hougaard, M.-L. T. Lee, and G. Whitmore. Analysis of overdispersed count data by mixtures of poisson variables and poisson processes. Biometrics, pages 1225–1238, 1997.[87] Instituto Nacional de Estadísticas, Servicio de Registro Civil e Identificación, Departamento de Estadísticas e Información de Salud, Consultado el: 2019-04-10.[88] M. J. Crowder. Beta-binomial anova for proportions. Applied Statistics, 27:34, 01 1978.[89] P. A. Jacobs and P. A. Lewis. Discrete time series generated by mixtures. i: Correlational and runs properties. Journal of the Royal Statistical Society: Series B (Methodological), 40(1):94–105, 1978.[90] H. Joe and R. Zhu. Generalized poisson distribution: the property of mixture of poisson and comparison with negative binomial distribution. Biometrical Journal: Journal of Mathematical Methods in Biosciences, 47(2):219–229, 2005[91] B. Jorgensen. Exponential dispersion models. Journal of the Royal Statistical Society. Series B (Methodological), 49(2):127–162, 1987.[92] B. Jorgensen. The theory of dispersion models. CRC Press, 1997.[93] B. Jørgensen and C. Kokonendji. Discrete dispersion models and their tweedie asymptotics. AStA Advances in Statistical Analysis, 100, 09 2014.[94] B. Kedem and K. Fokianos. Regression models for time series analysis, volume 488. John Wiley & Sons, 2005.[95] R. Kenett. ’the com-poisson model for count data: A survey of methods and applications’ by k. sellers, s. borle and g. shmueli. Applied Stochastic Models in Business and Industry, 28:117–121, 03 2012[96] J. F. C. Kingman. Poisson processes, volume 3 of Oxford Studies in Probability. The Clarendon Press Oxford University Press, New York, 1993. Oxford Science Publications.[97] C. C. Kokonendji. Over-and underdispersion models. Methods and applications of statistics in clinical Trials: Planning, analysis, and inferential methods, 2:506–526, 2014.[98] C. C. Kokonendji, C. G. Demétrio, and S. S. Zocchi. On hinde–demétrio regression models for overdispersed count data. Statistical Methodology, 4(3):277–291, 2007.[99] B. O. Koopman. On distributions admitting a sufficient statistic. Transactions of the American Mathematical Society, 39(3):399–409, 1936.[100] L. Kuo and T. Yang. Bayesian computation for nonhomogeneous poisson process in software reliability. Journal of the American Statistical Association, 91:763–773, 1996.[101] D. Lambert. Zero-inflated poisson regression, with an application to defects in manufacturing. Technometrics, 34:1–14, 02 1992.[102] A. Latour. Existence and stochastic structure of a non-negative integer-valued autoregressive process. Journal of Time Series Analysis, 19(4):439–455, 1998.[103] J. F. Lawless. Negative binomial and mixed poisson regression. The Canadian Journal of Statistics/La Revue Canadienne de Statistique, pages 209–225, 1987.[104] B. Li, P. McCullagh, et al. Potential functions and conservative estimating functions. The Annals of Statistics, 22(1):340–356, 1994.[105] Q. Li, H. Lian, and F. Zhu. Robust closed-form estimators for the integer-valued garch (1, 1) model. Computational Statistics & Data Analysis, 101:209–225, 2016.[106] L. Lin. Quasi bayesian likelihood. Statistical Methodology, 3(4):444–455, 2006.[107] D. V. Lindley. Fiducial distributions and bayes’ theorem. Journal of the Royal Statistical Society. Series B (Methodological), pages 102–107, 1958.[108] J. Lindsey. Modelling Frequency and Count Data. Oxford University Press, Oxford, 1995.[109] D. Lord, S. D. Guikema, et al. The conway–maxwell–poisson model for analyzing crash data. Applied Stochastic Models in Business and Industry, 28(2):122–127, 2012.[110] I. L. MacDonald and W. Zucchini. Hidden Markov and other models for discrete-valued time series, volume 110. CRC Press, 1997.[111] H. Mao, F. Zhu, and Y. Cui. A generalized mixture integer-valued garch model. Statistical Methods & Applications, 29(3):527–552, 2020.[112] B. H. Margolin, N. Kaplan, and E. Zeiger. Statistical analysis of the ames salmonella/microsome test. Proceedings of the National Academy of Sciences, 78(6):3779–3783, 1981.[113] B. H. Margolin, N. Kaplan, and E. Zeiger. Statistical analysis of the ames salmonella/microsome test. Proceedings of the National Academy of Sciences, 78(6):3779–3783, 1981.[114] C. Mcalpine, J. R. Rhodes, M. E. Bowen, D. Lunney, J. G. Callaghan, D. Mitchell, and H. Possingham. Can multiscale models of species distribution be generalized from region to region? a case study of the koala. Journal of Applied Ecology, 45, 04 2008.[115] P. McCullagh. Quasi-likelihood functions. The Annals of Statistics, pages 59–67, 1983.[116] P. McCullagh and J. Nelder. Generalized Linear Models, Second Edition. Chapman and Hall/CRC Monographs on Statistics and Applied Probability Series. Chapman & Hall, 1989.[117] E. McKenzie. Autoregressive moving-average processes with negative-binomial and geometric marginal distributions. Advances in Applied Probability, pages 679–705, 1986.[118] N. Metropolis and S. Ulam. The monte carlo method. Journal of the American statistical association, 44(247):335–341, 1949.[119] S.-P. Miaou. The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accident Analysis & Prevention, 26(4):471–482, 1994.[120] J. Mullahy. Specification and testing of some modified count data models. Journal of Econometrics, 33(3):341 – 365, 1986.[121] J. Mullahy. Heterogeneity, excess zeros, and the structure of count data models. Journal of Applied Econometrics, 12:337–50, 05 1997.[122] J. D. Musa and K. Okumoto. A logarithmic poisson execution time model for software reliability measurement. In Proceedings of the 7th international conference on Software engineering, pages 230– 238. Citeseer, 1984.[123] S. Nakajima, K. Watanabe, and M. Sugiyama. Variational Bayesian learning theory. Cambridge University Press, 2019.[124] B. H. Neelon, A. J. O’Malley, and S.-L. T. Normand. A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use. Statistical Modelling, 10(4):421–439, 2010.[125] J. A. Nelder and D. Pregibon. An extended quasi-likelihood function. Biometrika, 74(2):221–232, 1987.[126] J. A. Nelder and R. W. Wedderburn. Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3):370–384, 1972.[127] A. Noack. A class of random variables with discrete distributions. Ann. Math. Statist., 21(1):127–132, 03 1950.[128] K. Okumoto and A. L. Goel. Optimum release time for software systems based on reliability and cost criteria. Journal of Systems and Software, 1:315–318, 1979.[129] F. W. Olver, D. W. Lozier, R. F. Boisvert, and C. W. Clark. Nist handbook of mathematical functions hardback and cd-rom. chapter 8, pages 173–192. Cambridge university press, 2010.[130] G. Patil. Certain properties of the generalized power series distribution. Annals Inst. Statist. Math., Tokyo, 14:179–182, 01 1962.[131] G. P. Patil. Maximum likelihood estimation for generalized power series distributions and its application to a truncated binomial distribution. Biometrika, 49(1/2):227–237, 1962.[132] K. Pearson. Tables of the incomplete beta function. Technical report, 1934.[133] R. Petterle, W. Bonat, C. Kokonendji, J. Seganfredo, A. Moraes, and M. Silva. Double poisson-tweedie regression models. The International Journal of Biostatistics, 15, 04 2019.[134] A. Pievatolo and F. Ruggeri. Bayesian reliability analysis of complex repairable systems. Applied Stochastic Models in Business and Industry, 20:253–264, 2004.[135] E. J. G. Pitman. Sufficient statistics and intrinsic accuracy. Mathematical Proceedings of the Cambridge Philosophical Society, 32(4):567–579, 1936.[136] J. M. Potts and J. Elith. Comparing species abundance models. Ecological Modelling, 199(2):153–163, 2006.[137] M. B. Priestley. Spectral analysis and time series: probability and mathematical statistics. Number 04; QA280, P7. 1981.[138] P. Puig and J. Valero. Count data distributions: some characterizations with applications. Journal of the American Statistical Association, 101(473):332–340, 2006.[139] M. Pujol, J.-F. Barquinero, P. Puig, R. Puig, M. R. Caballín, and L. Barrios. A new model of biodosimetry to integrate low and high doses. PLoS One, 9(12):e114137, 2014.[140] S. Quine et al. Achievement orientation of aboriginal and white australian adolescents. 1973.[141] A. Quintero-Sarmiento, E. Cepeda-Cuervo, and V. Núñez Antón. Estimating infant mortality in colombia: Some overdispersion modelling approaches. Journal of Applied Statistics, 39:1011–1036, 05 2012.[142] J. Ramirez-Cid and J. Achcar. Bayesian inference for non-homogeneous poisson process in software reliability models. Computational Statistics and Data Analysis, 32:147–159, 1999.[143] J. E. Ramírez-Cid and J. A. Achcar. Bayesian inference for nonhomogeneous poisson processes in software reliability models assuming nonmonotonic intensity functions. Computational statistics & data analysis, 32(2):147–159, 1999.[144] M. Ridout, C. Demétrio, and J. Hinle. Models for count data with many zeros. international biomeric conference. Cape Town, 13:1–13, 01 1998.[145] S. Ross, J. Kelly, K. M. R. Collection, R. Sullivan, W. Perry, D. Mercer, R. Davis, T. Washburn, V. Sager, J. Boyce, et al. Stochastic Processes. Number v. 1 in Probability and Statistics Series. Wiley, 1983.[146] A. Saei, J. Ward, and C. A McGilchrist. Threshold models in a methadone programme evaluation. Statistics in medicine, 15:2253–60, 10 1996.[147] M. Sankaran. 275. note: The discrete poisson-lindley distribution. Biometrics, 26(1):145–149, 1970.[148] G. Seber and C. Wild. Nonlinear Regression. Wiley Series in Probability and Statistics. Wiley, 2005.[149] G. Shmueli, T. Minka, J. Kadane, S. Borle, and P. Boatwright. A useful distribution for fitting discrete data: Revival of the conway-maxwell-poisson distribution. Journal of the Royal Statistical Society Series C, 54:127–142, 02 2005.[150] R. H. Shumway and D. S. Stoffer. Time series analysis and its applications. Studies In Informatics And Control, 9(4):375–376, 2000.[151] A. Singh and G. Roberts. State space modelling of cross-classified time series of counts. International Statistical Review/Revue Internationale de Statistique, pages 321–335, 1992.[152] D. Spiegelhalter, A. Thomas, N. Best, and D. Lunn. WinBUGS user manual. MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK, 2003[153] D. Spiegelhalter, A. Thomas, N. Best, D. Lunn, and K. Rice. OpenBUGS examples, volume i. 2014.[154] F. W. Steutel and K. van Harn. Discrete analogues of self-decomposability and stability. The Annals of Probability, pages 893–899, 1979.[155] M. A. Tanner and W. Hung Wong. The calculation of posterior distributions by data augmentation: Rejoinder. Journal of The American Statistical Association, 82:528–540, 06 1987.[156] N. M. Temme. Special functions: An introduction to the classical functions of mathematical physics. John Wiley & Sons, 2011.[157] Zero-Inflated Poisson Regression and R Data Analysis Examples @ONLINE, Consultado el: 2019-03-27.[158] L. von Bortkiewicz and V. I. Bortkevič. Das gesetz der kleinen zahlen. BG Teubner, 1898.[159] R. W. M. Wedderburn. Quasi-likelihood functions, generalized linear models, and the gauss-newton method. Biometrika, 61(3):439–447, 1974.[160] C. H.Weiß. The inarch (1) model for overdispersed time series of counts. Communications in Statistics- Simulation and Computation, 39(6):1269–1291, 2010.[161] C. H. Weiß. Integer-valued autoregressive models for counts showing underdispersion. Journal of Applied Statistics, 40(9):1931–1948, 2013.[162] A.Welsh, R. Cunningham, C. Donnelly, and D. Lindenmayer. Modelling the abundance of rare species: statistical models for counts with extra zeros. Ecological Modelling, 88(1):297–308, 1996.[163] M.West, P. J. Harrison, and H. S. Migon. Dynamic generalized linear models and bayesian forecasting. Journal of the American Statistical Association, 80(389):73–83, 1985.[164] D. Williams. Extra-binomial variation in logistic linear models. Appl.Statist., 2:144–188, 1982. [165] R. Winkelmann. Duration dependence and dispersion in count-data models. Journal of Business & Economic Statistics, 13(4):467–474, 1995.[166] E. Xekalaki. Under-and overdispersion. Wiley StatsRef: Statistics Reference Online, 2014.[167] M. Xie and G. Hong. Ch. 28. software reliability modeling, estimation and analysis. In Advances in Reliability, volume 20 of Handbook of Statistics, pages 707 – 731. Elsevier, 2001.[168] T. Y. Yang and L. Kuo. Bayesian computation for the superposition of nonhomogeneous poisson processes. Canadian Journal of Statistics, 27(3):547–556, 1999.[169] S. L. Zeger. A regression model for time series of counts. Biometrika, 75(4):621–629, 1988.[170] S. L. Zeger and B. Qaqish. Markov regression models for time series: a quasi-likelihood approach. Biometrics, pages 1019–1031, 1988.[171] F. Zhu. A negative binomial integer-valued garch model. Journal of Time Series Analysis, 32(1):54–67, 2011.[172] F. Zhu. Modeling overdispersed or underdispersed count data with generalized poisson integer-valued garch models. Journal of Mathematical Analysis and Applications, 389(1):58–71, 2012.[173] F. Zhu, Q. Li, and D. Wang. A mixture integer-valued arch model. Journal of Statistical Planning and inference, 140(7):2025–2036, 2010.[174] F. Zhu, L. Shi, and S. Liu. Influence diagnostics in log-linear integer-valued garch models. AStA Advances in Statistical Analysis, 99(3):311–335, 2015.[175] A. Zuur, E. Ieno, N. Walker, A. Saveliev, and G. Smith. Mixed Effects Models and Extensions in Ecology with R. Statistics for Biology and Health. Springer New York, 2009.AdministradoresPúblico generalLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80388/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51ORIGINAL1013597805.2021.pdf1013597805.2021.pdfTesis de Doctorado en Ciencias - Matemáticasapplication/pdf5108767https://repositorio.unal.edu.co/bitstream/unal/80388/2/1013597805.2021.pdf60db03f5d66fcc94f313375fd6d1a63aMD52THUMBNAIL1013597805.2021.pdf.jpg1013597805.2021.pdf.jpgGenerated Thumbnailimage/jpeg3611https://repositorio.unal.edu.co/bitstream/unal/80388/3/1013597805.2021.pdf.jpgd8c0ba9ca80ed4dad5d1139bbd94fd4cMD53unal/80388oai:repositorio.unal.edu.co:unal/803882024-07-31 23:10:33.728Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Procesos de conteo, sobredispersión y extensiones

Publicaciones similares