Modelamiento espacio temporal de los desembarcos pesqueros en el Pacífico colombiano usando datos funcionales
ilustraciones, diagramas
- Autores:
-
Ramirez Yara, Yessica Natalia
- 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/86057
- Palabra clave:
- 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
ANALISIS FUNCIONAL
Functional analysis
Datos funcionales
Análisis espacial
Desembarcos pesqueros
Series de tiempo
Modelos ARIMA con covariables
Functional data analysis
Spatial analysis
Fish landings
Time series
ARIMA models with covariates
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Modelamiento espacio temporal de los desembarcos pesqueros en el Pacífico colombiano usando datos funcionales |
| dc.title.translated.eng.fl_str_mv |
Spatiotemporal modeling of fishing landings in the Colombian Pacific using functional data |
| title |
Modelamiento espacio temporal de los desembarcos pesqueros en el Pacífico colombiano usando datos funcionales |
| spellingShingle |
Modelamiento espacio temporal de los desembarcos pesqueros en el Pacífico colombiano usando datos funcionales 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas ANALISIS FUNCIONAL Functional analysis Datos funcionales Análisis espacial Desembarcos pesqueros Series de tiempo Modelos ARIMA con covariables Functional data analysis Spatial analysis Fish landings Time series ARIMA models with covariates |
| title_short |
Modelamiento espacio temporal de los desembarcos pesqueros en el Pacífico colombiano usando datos funcionales |
| title_full |
Modelamiento espacio temporal de los desembarcos pesqueros en el Pacífico colombiano usando datos funcionales |
| title_fullStr |
Modelamiento espacio temporal de los desembarcos pesqueros en el Pacífico colombiano usando datos funcionales |
| title_full_unstemmed |
Modelamiento espacio temporal de los desembarcos pesqueros en el Pacífico colombiano usando datos funcionales |
| title_sort |
Modelamiento espacio temporal de los desembarcos pesqueros en el Pacífico colombiano usando datos funcionales |
| dc.creator.fl_str_mv |
Ramirez Yara, Yessica Natalia |
| dc.contributor.advisor.none.fl_str_mv |
Arunachalam, Viswanathan |
| dc.contributor.author.none.fl_str_mv |
Ramirez Yara, Yessica Natalia |
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Ramirez Yara, Yessica Natalia [0000000196345406] |
| dc.contributor.cvlac.spa.fl_str_mv |
RAMIREZ YARA, YESSICA NATALIA [0000094819] |
| dc.contributor.researchgate.spa.fl_str_mv |
Ramirez Yara, Yessica [Yessica-Ramirez-Yara] |
| dc.subject.ddc.spa.fl_str_mv |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas |
| topic |
510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas ANALISIS FUNCIONAL Functional analysis Datos funcionales Análisis espacial Desembarcos pesqueros Series de tiempo Modelos ARIMA con covariables Functional data analysis Spatial analysis Fish landings Time series ARIMA models with covariates |
| dc.subject.lemb.none.fl_str_mv |
ANALISIS FUNCIONAL Functional analysis |
| dc.subject.proposal.spa.fl_str_mv |
Datos funcionales Análisis espacial Desembarcos pesqueros Series de tiempo Modelos ARIMA con covariables |
| dc.subject.proposal.eng.fl_str_mv |
Functional data analysis Spatial analysis Fish landings Time series ARIMA models with covariates |
| description |
ilustraciones, diagramas |
| publishDate |
2023 |
| dc.date.issued.none.fl_str_mv |
2023-05-24 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-08T21:24:35Z |
| dc.date.available.none.fl_str_mv |
2024-05-08T21:24:35Z |
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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/86057 |
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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/86057 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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[Aguilera and Aguilera-Morillo, 2013] Aguilera, A. and Aguilera-Morillo, M. (2013). Comparative study of different b-spline approaches for functional data. Mathematical and Computer Modelling, 58(7):1568–1579. [Bohn, 2005] Bohn, M. (2005). Univariate time series analysis;arima models. Econometrics 2. [Boh´orquez et al., 2016] Boh´orquez, M., Giraldo, R., and Mateu, J. (2016). Optimal sampling for spatial prediction of functional data. Statistical Methods & Applications, 25(1):39–54. [Box et al., 2015] Box, G., Jenkins, G., Reinsel, G., and Ljung, G. (2015). Time Series Analysis: Forecasting and Control. Wiley Series in Probability and Statistics. Wiley. [Brockwell and Davis, 1996] Brockwell, P. J. and Davis, R. A. (1996). Introduction to time series and forecasting / Peter J. Brockwell and Richard A. Davis. Springer New York. [Campos et al., 2016] Campos, R., Cremona, M., Pini, A., Chiaromonte, F., and Makova, K. (2016). Integration and fixation preferences of human and mouse endogenous retroviruses uncovered with functional data analysis. . PLoS Comput Biol, 12(6). [de Rivera, 1989] de Rivera, D. (1989). Estad´ıstica: Modelos y M´etodos. Alianza Universidad Textos Series. Alianza Editorial, S. A. [FAO, 2017] FAO (2017). Food and agriculture organization of the united nations. [Fortin, 2015] Fortin, D. (2015). Contributions to modeling spatially indexed functional data using a reproducing kernel Hilbert space framework. Tesis de doctorado, Iowa State University. [Ghose, 2018] Ghose, R. (2018). 1.29 - defining public participation gis. In Huang, B., editor, Comprehensive Geographic Information Systems, pages 431–437. Elsevier, Oxford. [Goodchild, 2009] Goodchild, M. (2009). Geographic information systems and science: Today and tomorrow. Procedia Earth and Planetary Science, 1:1037–1043. [Hamza et al., 2021] Hamza, F., Valsala, V., Mallissery, A., and George, G. (2021). Climate impacts on the landings of indian oil sardine over the south-eastern arabian sea. Fish and Fisheries, 22(1):175–193. [Herrera Montiel et al., 2019] Herrera Montiel, S. A., Coronado-Franco, K. V., and Selvaraj, J. J. (2019). Predicted changes in the potential distribution of seerfish (scomberomorus sierra) under multiple climate change scenarios in the colombian pacific ocean. Ecological Informatics, 53:100985. [Hormann et al., 2015] Hormann, S., Kidzinski, L., and Hallin, M. (2015). Dynamic functional principal components. Journal of the Royal Statistical Society: Series B: Statistical Methodology, 319-348. [Horv´ath and Kokoszka, 2012] Horv´ath, L. and Kokoszka, P. (2012). Inference for Functional Data with Applications. Springer Series in Statistics. Springer New York. [Hyndman and Ullah, 2007] Hyndman, R. and Ullah, M. (2007). Robust forecasting of mortality and fertility rates: A functional data approach. Computational Statistics & Data Analysis, 51(10):4942–4956. [Kuenzer et al., 2021] Kuenzer, T., H¨ormann, S., and Kokoszka, P. (2021). Principal component analysis of spatially indexed functions. Journal of the American Statistical Association, 116(535), 1444-1456. [Lewis, 1980] Lewis, E. (1980). The practical salinity scale 1978 and its antecedents. IEEE J. Ocean. Eng, OE-5(1): 3-8. [Liu et al., 2016] Liu, C., Ray, S., and Hooker, G. (2016). Functional principal component analysis of spatially correlated data. Statistics and Computing, 1:1–16. [Makridakis et al., 1983] Makridakis, S., Wheelwright, S., and McGee, V. (1983). Forecasting: Methods and Applications. Wiley series in management. Wiley. [Marco et al., 2021] Marco, J., Valderrama, D., and Rueda, M. (2021). Evaluating management reforms in a colombian shrimp fishery using fisheries performance indicators. Marine Policy, 125:104258. [Martínez, 2020] Martínez, E. (2020). Un modelo estocástico para analizar los efectos de la variación de la temperatura sobre la captura pesquera a lo largo de la costa del Pacífico Colombiano. Master’s thesis, Universidad Nacional de Colombia. [Montgomery et al., 2015] Montgomery, D. C., Jennings, C. L., and Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons. [Moriarity et al., 2020] Moriarity, R. J., Liberda, E. N., and Tsuji, L. J. (2020). Using a geographic information system to assess local scale methylmercury exposure from fish in nine communities of the eeyou istchee territory (james bay, quebec, canada). Environmental Research, 191:110147. [Nieto and Mélin, 2017] Nieto, K. and Mélin, F. (2017). Variability of chlorophyll-a concentration in the gulf of guinea and its relation to physical oceanographic variables. Progress in Oceanography, 151:97–115. [Ojeda and Arias, 2000] Ojeda, L. and Arias, R. (2000). Informe nacional sobre la gestión de agua en colombia (recursos hídricos, agua potable y saneamiento). Ministerio de Medio Ambiente, Santafé de Bogotá, page 137. [Pawlowicz, 2013] Pawlowicz, R. (2013). Key physical variables in the ocean: Temperature, salinity, and density. Nature Education Knowledge, 4(4):13. [Ramsay, 2006] Ramsay, J. (2006). Functional data analysis. Springer New York. [Ramsay and Silverman, 2005] Ramsay, J. and Silverman, B. (2005). Functional Data Analysis. Springer Series in Statistics. Springer. [Santos et al., 2020] Santos, E. F., Barbosa, A. L., and Duarte-Neto, P. J. (2020). Global correlation matrix spectra of the surface temperature of the oceans from random matrix theory to poisson fluctuations. Physics Letters A, 384(27):126689. [Selvaraj et al., 2017] Selvaraj, J., Coronado-Franco, K., and Guzm´an, A. (2017). Caracterización espacial y temporal de la salinidad y temperatura en bancos de pesca del océano pacífico colombiano. Congreso Latinoamericano de Ciencias del Mar - COLACMAR 2017. [Selvaraj et al., 2018] Selvaraj, J., Coronado-Franco, K., and Guzmán, A. (2018). Projected sea surface temperature changes in the fishing areas of the colombian pacific under climate change scenarios. 4th International symposium: The effects of climate change on the World´s Oceans. [Selvaraj et al., 2020] Selvaraj, J. J., Arunachalam, V., Coronado-Franco, K. V., RomeroOrjuela, L. V., and Ramírez-Yara, Y. N. (2020). Time-series modeling of fishery landings in the colombian pacific ocean using an arima model. Regional Studies in Marine Science, 39:101477. [Shang and Kearney, 2022] Shang, H. L. and Kearney, F. (2022). Dynamic functional timeseries forecasts of foreign exchange implied volatility surfaces. International Journal of Forecasting, 38(3):1025–1049. [Van den Bossche et al., 2007] Van den Bossche, F., Wets, G., and Brijs, T. (2007). A regression model with arima errors to investigate the frequency and severity of road traffic accidents. [Venkatramanan et al., 2019] Venkatramanan, S., Prasanna, M., and Chung, S. (2019). GIS and Geostatistical Techniques for Groundwater Science. Elsevier. [Wang et al., 2016] Wang, J., Chiou, J., and M¨uller, H. (2016). Functional data analysis. Annual Review of Statistics and Its Application, 3(1):257–295. |
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Facultad de Ciencias |
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Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Arunachalam, Viswanathanc4c758748e94de0301844d7c2a049050Ramirez Yara, Yessica Natalia32ba9e769d9a2a14d39eb181f16ccdf6Ramirez Yara, Yessica Natalia [0000000196345406]RAMIREZ YARA, YESSICA NATALIA [0000094819]Ramirez Yara, Yessica [Yessica-Ramirez-Yara]2024-05-08T21:24:35Z2024-05-08T21:24:35Z2023-05-24https://repositorio.unal.edu.co/handle/unal/86057Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEl documento a continuación tiene como objetivo estimar modelos estocásticos de series de tiempo ARIMA para las toneladas de especies de peces más importantes a nivel social y económico en el Pacífico colombiano, capturadas por medio de pesca. Se pretende usar como covariables de los modelos de producción por especie, las curvas de salinidad y temperatura medidas a -0.5, -41 y -86 metros bajo la superficie del océano, las cuales fueron calculadas usando herramientas de análisis espacial y análisis de datos funcionales, tomando como referencia para cada especie, el área formada por los pixeles con los valores más altos de probabilidad del ráster de presencia de especies y los archivos NetCDF del Pacífico de temperatura y salinidad para distintas profundidades; con el fin de explicar el comportamiento de la producción de desembarcos por especie en función de los cambios de las variables oceanográficas. (Texto tomado de la fuente)The following document aims to estimate stochastic ARIMA time series models for the production in tons of fishing landings of the most socially and economically important fish species in the Colombian Pacific. It is intended to use as covariates of the production models by species, the salinity and temperature curves measured at -0.5, -41 and -86 meters under the ocean surface, which were calculated using spatial analysis tools and functional data analysis, taking as a reference for each species, the area formed by the pixels with the highest probability values of the species presence raster and the Pacific NetCDF files of temperature and salinity for different depths; in order to be able to explain the behavior of the production of landings by species as a function of changes in oceanographic variables.MaestríaMagíster en Ciencias - EstadísticaAnálisis espacial, Series de tiempo, Datos funcionalesxiv, 74 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasANALISIS FUNCIONALFunctional analysisDatos funcionalesAnálisis espacialDesembarcos pesquerosSeries de tiempoModelos ARIMA con covariablesFunctional data analysisSpatial analysisFish landingsTime seriesARIMA models with covariatesModelamiento espacio temporal de los desembarcos pesqueros en el Pacífico colombiano usando datos funcionalesSpatiotemporal modeling of fishing landings in the Colombian Pacific using functional dataTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM[Aguilera and Aguilera-Morillo, 2013] Aguilera, A. and Aguilera-Morillo, M. (2013). Comparative study of different b-spline approaches for functional data. Mathematical and Computer Modelling, 58(7):1568–1579.[Bohn, 2005] Bohn, M. (2005). Univariate time series analysis;arima models. Econometrics 2.[Boh´orquez et al., 2016] Boh´orquez, M., Giraldo, R., and Mateu, J. (2016). Optimal sampling for spatial prediction of functional data. Statistical Methods & Applications, 25(1):39–54.[Box et al., 2015] Box, G., Jenkins, G., Reinsel, G., and Ljung, G. (2015). Time Series Analysis: Forecasting and Control. Wiley Series in Probability and Statistics. Wiley.[Brockwell and Davis, 1996] Brockwell, P. J. and Davis, R. A. (1996). Introduction to time series and forecasting / Peter J. Brockwell and Richard A. Davis. Springer New York.[Campos et al., 2016] Campos, R., Cremona, M., Pini, A., Chiaromonte, F., and Makova, K. (2016). Integration and fixation preferences of human and mouse endogenous retroviruses uncovered with functional data analysis. . PLoS Comput Biol, 12(6).[de Rivera, 1989] de Rivera, D. (1989). Estad´ıstica: Modelos y M´etodos. Alianza Universidad Textos Series. Alianza Editorial, S. A.[FAO, 2017] FAO (2017). Food and agriculture organization of the united nations.[Fortin, 2015] Fortin, D. (2015). Contributions to modeling spatially indexed functional data using a reproducing kernel Hilbert space framework. Tesis de doctorado, Iowa State University.[Ghose, 2018] Ghose, R. (2018). 1.29 - defining public participation gis. In Huang, B., editor, Comprehensive Geographic Information Systems, pages 431–437. Elsevier, Oxford.[Goodchild, 2009] Goodchild, M. (2009). Geographic information systems and science: Today and tomorrow. Procedia Earth and Planetary Science, 1:1037–1043.[Hamza et al., 2021] Hamza, F., Valsala, V., Mallissery, A., and George, G. (2021). Climate impacts on the landings of indian oil sardine over the south-eastern arabian sea. Fish and Fisheries, 22(1):175–193.[Herrera Montiel et al., 2019] Herrera Montiel, S. A., Coronado-Franco, K. V., and Selvaraj, J. J. (2019). Predicted changes in the potential distribution of seerfish (scomberomorus sierra) under multiple climate change scenarios in the colombian pacific ocean. Ecological Informatics, 53:100985.[Hormann et al., 2015] Hormann, S., Kidzinski, L., and Hallin, M. (2015). Dynamic functional principal components. Journal of the Royal Statistical Society: Series B: Statistical Methodology, 319-348.[Horv´ath and Kokoszka, 2012] Horv´ath, L. and Kokoszka, P. (2012). Inference for Functional Data with Applications. Springer Series in Statistics. Springer New York.[Hyndman and Ullah, 2007] Hyndman, R. and Ullah, M. (2007). Robust forecasting of mortality and fertility rates: A functional data approach. Computational Statistics & Data Analysis, 51(10):4942–4956.[Kuenzer et al., 2021] Kuenzer, T., H¨ormann, S., and Kokoszka, P. (2021). Principal component analysis of spatially indexed functions. Journal of the American Statistical Association, 116(535), 1444-1456.[Lewis, 1980] Lewis, E. (1980). The practical salinity scale 1978 and its antecedents. IEEE J. Ocean. Eng, OE-5(1): 3-8.[Liu et al., 2016] Liu, C., Ray, S., and Hooker, G. (2016). Functional principal component analysis of spatially correlated data. Statistics and Computing, 1:1–16.[Makridakis et al., 1983] Makridakis, S., Wheelwright, S., and McGee, V. (1983). Forecasting: Methods and Applications. Wiley series in management. Wiley.[Marco et al., 2021] Marco, J., Valderrama, D., and Rueda, M. (2021). Evaluating management reforms in a colombian shrimp fishery using fisheries performance indicators. Marine Policy, 125:104258.[Martínez, 2020] Martínez, E. (2020). Un modelo estocástico para analizar los efectos de la variación de la temperatura sobre la captura pesquera a lo largo de la costa del Pacífico Colombiano. Master’s thesis, Universidad Nacional de Colombia.[Montgomery et al., 2015] Montgomery, D. C., Jennings, C. L., and Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.[Moriarity et al., 2020] Moriarity, R. J., Liberda, E. N., and Tsuji, L. J. (2020). Using a geographic information system to assess local scale methylmercury exposure from fish in nine communities of the eeyou istchee territory (james bay, quebec, canada). Environmental Research, 191:110147.[Nieto and Mélin, 2017] Nieto, K. and Mélin, F. (2017). Variability of chlorophyll-a concentration in the gulf of guinea and its relation to physical oceanographic variables. Progress in Oceanography, 151:97–115.[Ojeda and Arias, 2000] Ojeda, L. and Arias, R. (2000). Informe nacional sobre la gestión de agua en colombia (recursos hídricos, agua potable y saneamiento). Ministerio de Medio Ambiente, Santafé de Bogotá, page 137.[Pawlowicz, 2013] Pawlowicz, R. (2013). Key physical variables in the ocean: Temperature, salinity, and density. Nature Education Knowledge, 4(4):13.[Ramsay, 2006] Ramsay, J. (2006). Functional data analysis. Springer New York.[Ramsay and Silverman, 2005] Ramsay, J. and Silverman, B. (2005). Functional Data Analysis. Springer Series in Statistics. Springer.[Santos et al., 2020] Santos, E. F., Barbosa, A. L., and Duarte-Neto, P. J. (2020). Global correlation matrix spectra of the surface temperature of the oceans from random matrix theory to poisson fluctuations. Physics Letters A, 384(27):126689.[Selvaraj et al., 2017] Selvaraj, J., Coronado-Franco, K., and Guzm´an, A. (2017). Caracterización espacial y temporal de la salinidad y temperatura en bancos de pesca del océano pacífico colombiano. Congreso Latinoamericano de Ciencias del Mar - COLACMAR 2017.[Selvaraj et al., 2018] Selvaraj, J., Coronado-Franco, K., and Guzmán, A. (2018). Projected sea surface temperature changes in the fishing areas of the colombian pacific under climate change scenarios. 4th International symposium: The effects of climate change on the World´s Oceans.[Selvaraj et al., 2020] Selvaraj, J. J., Arunachalam, V., Coronado-Franco, K. V., RomeroOrjuela, L. V., and Ramírez-Yara, Y. N. (2020). Time-series modeling of fishery landings in the colombian pacific ocean using an arima model. Regional Studies in Marine Science, 39:101477.[Shang and Kearney, 2022] Shang, H. L. and Kearney, F. (2022). Dynamic functional timeseries forecasts of foreign exchange implied volatility surfaces. International Journal of Forecasting, 38(3):1025–1049.[Van den Bossche et al., 2007] Van den Bossche, F., Wets, G., and Brijs, T. (2007). A regression model with arima errors to investigate the frequency and severity of road traffic accidents.[Venkatramanan et al., 2019] Venkatramanan, S., Prasanna, M., and Chung, S. (2019). GIS and Geostatistical Techniques for Groundwater Science. Elsevier.[Wang et al., 2016] Wang, J., Chiou, J., and M¨uller, H. (2016). Functional data analysis. 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