Estimación del tamaño del mercado de la telefonía móvil en Colombia a través de un modelo estadístico
En este trabajo se presenta el ajuste de un modelo estadístico para pronosticar el tamaño del mercado móvil en Colombia (medido en cantidad de líneas) a partir de la utilización de los datos simulados en una de las compañías representativas del sector, con la finalidad de poder tener información opo...
- Autores:
-
Londoño Ceballos, Catalina
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/84193
- Palabra clave:
- 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
Modelos lineales (Estadística)
Procesos de Poisson
Modelos lineales generalizados
Estimación de ecuaciones generalizadas
Modelos estadísticos
Pronóstico
Telefonía móvil
Generalized linear models
Generalized estimating equation
mobile telephony
Statistical models
Forecasting
Telefonía móvil
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Estimación del tamaño del mercado de la telefonía móvil en Colombia a través de un modelo estadístico |
| dc.title.translated.eng.fl_str_mv |
Estimation of the mobile telecommunication market size in Colombia through a statistical model |
| title |
Estimación del tamaño del mercado de la telefonía móvil en Colombia a través de un modelo estadístico |
| spellingShingle |
Estimación del tamaño del mercado de la telefonía móvil en Colombia a través de un modelo estadístico 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Modelos lineales (Estadística) Procesos de Poisson Modelos lineales generalizados Estimación de ecuaciones generalizadas Modelos estadísticos Pronóstico Telefonía móvil Generalized linear models Generalized estimating equation mobile telephony Statistical models Forecasting Telefonía móvil |
| title_short |
Estimación del tamaño del mercado de la telefonía móvil en Colombia a través de un modelo estadístico |
| title_full |
Estimación del tamaño del mercado de la telefonía móvil en Colombia a través de un modelo estadístico |
| title_fullStr |
Estimación del tamaño del mercado de la telefonía móvil en Colombia a través de un modelo estadístico |
| title_full_unstemmed |
Estimación del tamaño del mercado de la telefonía móvil en Colombia a través de un modelo estadístico |
| title_sort |
Estimación del tamaño del mercado de la telefonía móvil en Colombia a través de un modelo estadístico |
| dc.creator.fl_str_mv |
Londoño Ceballos, Catalina |
| dc.contributor.advisor.none.fl_str_mv |
SALAZAR URIBE, JUAN CARLOS |
| dc.contributor.author.none.fl_str_mv |
Londoño Ceballos, Catalina |
| dc.subject.ddc.spa.fl_str_mv |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas |
| topic |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Modelos lineales (Estadística) Procesos de Poisson Modelos lineales generalizados Estimación de ecuaciones generalizadas Modelos estadísticos Pronóstico Telefonía móvil Generalized linear models Generalized estimating equation mobile telephony Statistical models Forecasting Telefonía móvil |
| dc.subject.lemb.none.fl_str_mv |
Modelos lineales (Estadística) Procesos de Poisson |
| dc.subject.proposal.spa.fl_str_mv |
Modelos lineales generalizados Estimación de ecuaciones generalizadas Modelos estadísticos Pronóstico Telefonía móvil |
| dc.subject.proposal.eng.fl_str_mv |
Generalized linear models Generalized estimating equation mobile telephony Statistical models Forecasting |
| dc.subject.wikidata.none.fl_str_mv |
Telefonía móvil |
| description |
En este trabajo se presenta el ajuste de un modelo estadístico para pronosticar el tamaño del mercado móvil en Colombia (medido en cantidad de líneas) a partir de la utilización de los datos simulados en una de las compañías representativas del sector, con la finalidad de poder tener información oportuna para la toma de decisiones comerciales y tácticas que utilizan dicha información. Como resultado se logró obtener un modelo con márgenes mínimos de error mejorando respecto del modelo lineal normal de referencia, se obtuvo un error medio porcentual absoluto de 0.53 %. (texto tomado de la fuente) |
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2023 |
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2023-07-17T21:20:45Z |
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2023-07-17T21:20:45Z |
| dc.date.issued.none.fl_str_mv |
2023-07 |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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Text |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/84193 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/84193 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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LaReferencia |
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Agresti, A. (2002). Categorical data analysis. John Wiley & Sons. Agresti, A. (2015). Foundations of linear and generalized linear models. John Wiley & Sons. Annafari, M. T. (2013). Multiple subscriptions of mobile telephony: Explaining the diffusion pattern using sampling data. Telecommunications Policy, 37(10):930–939. Regulating and investment in new communications infrastructure Understanding ICT adoption and market trends: Papers from recent European ITS regional conferences Cameron, A. and Trivedi, P. (1999). Essentials of count data regression. A Companion to Theoretical Econometrics. Malden, MA: Blackwell Publishing Ltd. Chu, W.-L., Wu, F.-S., Kao, K.-S., and Yen, D. C. (2009). Diffusion of mobile telephony: An empirical study in Taiwan. Telecommunications Policy, 33(9):506–520. Dagum, E. B. and Cholette, P. A. (2006). Benchmarking, temporal distribution, and reconciliation methods for time series. de Myttenaere, A., Golden, B., Le Grand, B., and Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192:38–48. Advances in artificial neural networks, machine learning and computational intelligence. Fitzmaurice, G., Laird, N., and Ware, J. (2011). Applied Longitudinal Analysis. Wiley Series in Probability and Statistics. Wiley Fitzmaurice, G. M., Laird, N. M., and Ware, J. H. (2012). Applied longitudinal analysis, volume 998. John Wiley & Sons. Forthofer, R. N., Lee, E. S., and Hernandez, M. (2007). 3 - descriptive methods. In Forthofer, R. N., Lee, E. S., and Hernandez, M., editors, Biostatistics (Second Edition), pages 21– 69. Academic Press, San Diego, second edition edition. Frees, E. W. et al. (2004). Longitudinal and panel data: analysis and applications in the social sciences. Cambridge University Press. Gamboa, L. F. and Otero, J. (2009). An estimation of the pattern of diffusion of mobile phones: The case of Colombia. Telecommunications Policy, 33(10-11):611–620. Gamer, M., Lemon, J., and <puspendra.pusp22@gmail.com>, I. F. P. S. (2019). irr: Various Coefficients of Interrater Reliability and Agreement. R package version 0.84.1. Halekoh, U., Højsgaard, S., and Yan, J. (2006). The r package geepack for generalized estimating equations. Journal of Statistical Software, 15/2:1–11. Hardin, J. and Hilbe, J. (2013). Generalized estimating equations (second edition). Hastie, T. (2022). gam: Generalized Additive Models. R package version 1.20.2. Herrera Giraldo, M. F. (2012). Difusión de la telefonía móvil en Colombia. Master’s thesis. Iliinsky, N. and Steele, J. (2011). Designing data visualizations: Representing informational Relationships. O’ Reilly Media, Inc. Islama, M. R. (2014). R program for temporal disaggregation: Denton’s method. Jha, A. and Saha, D. (2020). Forecasting and analysing the characteristics of 3G and 4G mobile broadband diffusion in India: A comparative evaluation of Bass, Norton-Bass, Gompertz, and logistic growth models. Technological Forecasting and Social Change, 152:119885. Koo, T. K. and Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of chiropractic medicine, 15(2):155–163. Kristoufek, L. (2014). Measuring correlations between non-stationary series with dcca coefficient. Physica A: Statistical Mechanics and its Applications, 402:291–298. Li, G., Lian, H., Feng, S., and Zhu, L. (2013). Automatic variable selection for longitudinal generalized linear models. Computational Statistics & Data Analysis, 61:174–186. Liang, K.-Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1):13–22. Luo, R. and Pan, J. (2022). Conditional generalized estimating equations of mean-variancecorrelation for clustered data. Computational Statistics Data Analysis, 168:107386. Molnar, C. (2020). Interpretable machine learning. Lulu. com. Montgomery, D. C., Peck, E. A., and Vining, G. G. (2012). Introduction to linear regression analysis. John Wiley & Sons. Pan, W. (2001). Akaike’s information criterion in generalized estimating equations. Biometrics, 57(1):120–125. Park, T., Davis, C. S., and Li, N. (1998). Alternative gee estimation procedures for discrete longitudinal data. Computational Statistics Data Analysis, 28(3):243–256. Pearson, R. K. (2018). Exploratory data analysis using R. Chapman and Hall/CRC. Prass, T. S. and Pumi, G. (2020). DCCA: Detrended Fluctuation and Detrended CrossCorrelation Analysis. R package version 0.1.1. Puth, M.-T., Neuhäuser, M., and Ruxton, G. D. (2015). Effective use of spearman’s and kendall’s correlation coefficients for association between two measured traits. Animal Behaviour, 102:77–84 R Core Team (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Ruppert, D., Wand, M. P., and Carroll, R. J. (2003). Semiparametric regression. Cambridge University Press. Sax, C. and Steiner, P. (2013). Temporal disaggregation of time series. The R Journal, 5(2):80–87. Seabold, S. and Perktold, J. (2010). statsmodels: Econometric and statistical modeling with python. In 9th Python in Science Conference. Shen, C. (2015). Analysis of detrended time-lagged cross-correlation between two nonstationary time series. Physics Letters A, 379(7):680–687. Tsai, M.-Y. (2015). Comparison of concordance correlation coefficient via variance components, generalized estimating equations and weighted approaches with model selection. Computational Statistics Data Analysis, 82:47–58. Tsai, M.-Y., Wang, J.-F., and Wu, J.-L. (2011). Generalized estimating equations with model selection for comparing dependent categorical agreement data. Computational Statistics Data Analysis, 55(7):2354–2362. Upton, G. and Cook, I. (2014). A dictionary of statistics 3e. Oxford university press. Wold, S. (1974). Spline functions in data analysis. Technometrics, 16(1):1–11. Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Cengage learning. Wu, F.-S. and Chu, W.-L. (2010). Diffusion models of mobile telephony. Journal of Business Research, 63(5):497–501. TECHNOLOGY MANAGEMENT. Zebende, G. (2011). Dcca cross-correlation coefficient: Quantifying level of cross-correlation. Physica A: Statistical Mechanics and its Applications, 390(4):614–618. Ziegler, A. (2011). Generalized estimating equations, volume 204. Springer Science & Business Media. |
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Universidad Nacional de Colombia |
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Medellín - Ciencias - Maestría en Ciencias - Estadística |
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Facultad de Ciencias |
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Medellín, Colombia |
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Universidad Nacional de Colombia - Sede Medellín |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2SALAZAR URIBE, JUAN CARLOS044ca78a0383627f681bf97fa37d9be0600Londoño Ceballos, Catalinabfa28a1eaeae144cf1ca89bdec7a6e692023-07-17T21:20:45Z2023-07-17T21:20:45Z2023-07https://repositorio.unal.edu.co/handle/unal/84193Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/En este trabajo se presenta el ajuste de un modelo estadístico para pronosticar el tamaño del mercado móvil en Colombia (medido en cantidad de líneas) a partir de la utilización de los datos simulados en una de las compañías representativas del sector, con la finalidad de poder tener información oportuna para la toma de decisiones comerciales y tácticas que utilizan dicha información. Como resultado se logró obtener un modelo con márgenes mínimos de error mejorando respecto del modelo lineal normal de referencia, se obtuvo un error medio porcentual absoluto de 0.53 %. (texto tomado de la fuente)This paper presents the adjustment of a statistical model to forecast the size of the mobile market in Colombia (measured in number of lines) from the use of simulated data in one of the representative companies of the sector, with the purpose of being able to have timely information for making business decisions and tactics that use such information. As a result, it was possible to obtain a model with minimum margins of error, improving with respect to the reference normal linear model, an average absolute percentage error of 0.53 % was obtained.MaestríaAnálisis Multivariado de DatosÁrea Curricular Estadística95 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasModelos lineales (Estadística)Procesos de PoissonModelos lineales generalizadosEstimación de ecuaciones generalizadasModelos estadísticosPronósticoTelefonía móvilGeneralized linear modelsGeneralized estimating equationmobile telephonyStatistical modelsForecastingTelefonía móvilEstimación del tamaño del mercado de la telefonía móvil en Colombia a través de un modelo estadísticoEstimation of the mobile telecommunication market size in Colombia through a statistical modelTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMLaReferenciaAgresti, A. (2002). Categorical data analysis. John Wiley & Sons.Agresti, A. (2015). Foundations of linear and generalized linear models. John Wiley & Sons.Annafari, M. T. (2013). Multiple subscriptions of mobile telephony: Explaining the diffusion pattern using sampling data. Telecommunications Policy, 37(10):930–939. Regulating and investment in new communications infrastructure Understanding ICT adoption and market trends: Papers from recent European ITS regional conferencesCameron, A. and Trivedi, P. (1999). Essentials of count data regression. A Companion to Theoretical Econometrics. Malden, MA: Blackwell Publishing Ltd.Chu, W.-L., Wu, F.-S., Kao, K.-S., and Yen, D. C. (2009). Diffusion of mobile telephony: An empirical study in Taiwan. Telecommunications Policy, 33(9):506–520.Dagum, E. B. and Cholette, P. A. (2006). Benchmarking, temporal distribution, and reconciliation methods for time series.de Myttenaere, A., Golden, B., Le Grand, B., and Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192:38–48. Advances in artificial neural networks, machine learning and computational intelligence.Fitzmaurice, G., Laird, N., and Ware, J. (2011). Applied Longitudinal Analysis. Wiley Series in Probability and Statistics. WileyFitzmaurice, G. M., Laird, N. M., and Ware, J. H. (2012). Applied longitudinal analysis, volume 998. John Wiley & Sons.Forthofer, R. N., Lee, E. S., and Hernandez, M. (2007). 3 - descriptive methods. In Forthofer, R. N., Lee, E. S., and Hernandez, M., editors, Biostatistics (Second Edition), pages 21– 69. Academic Press, San Diego, second edition edition.Frees, E. W. et al. (2004). Longitudinal and panel data: analysis and applications in the social sciences. Cambridge University Press.Gamboa, L. F. and Otero, J. (2009). An estimation of the pattern of diffusion of mobile phones: The case of Colombia. Telecommunications Policy, 33(10-11):611–620.Gamer, M., Lemon, J., and <puspendra.pusp22@gmail.com>, I. F. P. S. (2019). irr: Various Coefficients of Interrater Reliability and Agreement. R package version 0.84.1.Halekoh, U., Højsgaard, S., and Yan, J. (2006). The r package geepack for generalized estimating equations. Journal of Statistical Software, 15/2:1–11.Hardin, J. and Hilbe, J. (2013). Generalized estimating equations (second edition).Hastie, T. (2022). gam: Generalized Additive Models. R package version 1.20.2.Herrera Giraldo, M. F. (2012). Difusión de la telefonía móvil en Colombia. Master’s thesis.Iliinsky, N. and Steele, J. (2011). Designing data visualizations: Representing informational Relationships. O’ Reilly Media, Inc.Islama, M. R. (2014). R program for temporal disaggregation: Denton’s method.Jha, A. and Saha, D. (2020). Forecasting and analysing the characteristics of 3G and 4G mobile broadband diffusion in India: A comparative evaluation of Bass, Norton-Bass, Gompertz, and logistic growth models. Technological Forecasting and Social Change, 152:119885.Koo, T. K. and Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of chiropractic medicine, 15(2):155–163.Kristoufek, L. (2014). Measuring correlations between non-stationary series with dcca coefficient. Physica A: Statistical Mechanics and its Applications, 402:291–298.Li, G., Lian, H., Feng, S., and Zhu, L. (2013). Automatic variable selection for longitudinal generalized linear models. Computational Statistics & Data Analysis, 61:174–186.Liang, K.-Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1):13–22.Luo, R. and Pan, J. (2022). Conditional generalized estimating equations of mean-variancecorrelation for clustered data. Computational Statistics Data Analysis, 168:107386.Molnar, C. (2020). Interpretable machine learning. Lulu. com.Montgomery, D. C., Peck, E. A., and Vining, G. G. (2012). Introduction to linear regression analysis. John Wiley & Sons.Pan, W. (2001). Akaike’s information criterion in generalized estimating equations. Biometrics, 57(1):120–125.Park, T., Davis, C. S., and Li, N. (1998). Alternative gee estimation procedures for discrete longitudinal data. Computational Statistics Data Analysis, 28(3):243–256.Pearson, R. K. (2018). Exploratory data analysis using R. Chapman and Hall/CRC.Prass, T. S. and Pumi, G. (2020). DCCA: Detrended Fluctuation and Detrended CrossCorrelation Analysis. R package version 0.1.1.Puth, M.-T., Neuhäuser, M., and Ruxton, G. D. (2015). Effective use of spearman’s and kendall’s correlation coefficients for association between two measured traits. Animal Behaviour, 102:77–84R Core Team (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.Ruppert, D., Wand, M. P., and Carroll, R. J. (2003). Semiparametric regression. Cambridge University Press.Sax, C. and Steiner, P. (2013). Temporal disaggregation of time series. The R Journal, 5(2):80–87.Seabold, S. and Perktold, J. (2010). statsmodels: Econometric and statistical modeling with python. In 9th Python in Science Conference.Shen, C. (2015). Analysis of detrended time-lagged cross-correlation between two nonstationary time series. Physics Letters A, 379(7):680–687.Tsai, M.-Y. (2015). Comparison of concordance correlation coefficient via variance components, generalized estimating equations and weighted approaches with model selection. Computational Statistics Data Analysis, 82:47–58.Tsai, M.-Y., Wang, J.-F., and Wu, J.-L. (2011). Generalized estimating equations with model selection for comparing dependent categorical agreement data. Computational Statistics Data Analysis, 55(7):2354–2362.Upton, G. and Cook, I. (2014). A dictionary of statistics 3e. Oxford university press.Wold, S. (1974). Spline functions in data analysis. Technometrics, 16(1):1–11.Wooldridge, J. M. (2015). Introductory econometrics: A modern approach. Cengage learning.Wu, F.-S. and Chu, W.-L. (2010). Diffusion models of mobile telephony. Journal of Business Research, 63(5):497–501. TECHNOLOGY MANAGEMENT.Zebende, G. (2011). Dcca cross-correlation coefficient: Quantifying level of cross-correlation. Physica A: Statistical Mechanics and its Applications, 390(4):614–618.Ziegler, A. (2011). Generalized estimating equations, volume 204. Springer Science & Business Media.Receptores de fondos federales y solicitantesORIGINAL1020461755.2023.pdf1020461755.2023.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf2620577https://repositorio.unal.edu.co/bitstream/unal/84193/3/1020461755.2023.pdfebac2abd9838188929d778d192ca414dMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84193/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51THUMBNAIL1020461755.2023.pdf.jpg1020461755.2023.pdf.jpgGenerated Thumbnailimage/jpeg4359https://repositorio.unal.edu.co/bitstream/unal/84193/4/1020461755.2023.pdf.jpg8b3b89b7eb12a0291a04d4e91edb8c2eMD54unal/84193oai:repositorio.unal.edu.co:unal/841932023-08-11 23:04:37.769Repositorio Institucional Universidad Nacional de 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