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dc.rights.licenseAtribución-SinDerivadas 4.0 Internacional
dc.contributor.advisorMelo Martínez, Oscar Orlando
dc.contributor.authorAvellaneda García, Camilo Andrés
dc.date.accessioned2020-08-21T22:44:03Z
dc.date.available2020-08-21T22:44:03Z
dc.date.issued2020-02-01
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/78161
dc.description.abstractEn este documento se realiza una comparación de modelos de regresión para el caso en donde se tienen datos longitudinales con muestreo destructivo de unidades observacionales, las cuales provienen de unidades experimentales que son medidas en todos los tiempos del análisis. La comparación se hace a partir de modelos de regresión con efectos fijos y mixtos, entre los cuales se encuentra un símil que se utiliza para datos denominados como pseudo-panel, y uno de análisis de varianza multivariado. Para comparar los modelos se utilizó el cuadrado medio del error. Esto se realizó mediante simulación y una aplicación a datos de la vida real que hacen referencia a los puntajes en las pruebas Saber 11 aplicadas a estudiantes en Colombia.
dc.description.abstractIn this work I make a comparison of regression models for the case where we have longitudinal data with destructive sampling of observational units which come from experimental units that are measured in every time of the analysis. The comparision is made from linear models of fixed and mixed effects, using a model used for pseudo-panel data too, and one corresponding to the multivariate analysis of variance case. To carry out the comparison between the different models I use the mean square error. It was made through simulation and using the scores of the test Saber 11 applied to students in Colombia.
dc.format.extent69
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/
dc.subject.ddc519 - Probabilidades y matemáticas aplicadas
dc.titleAnálisis de datos longitudinales con muestreo destructivo:una perspectiva desde los modelos lineales mixtos
dc.title.alternativeAnalysis of longitudinal data with destructive sampling: a perspective using linear mixed models
dc.typeOtro
dc.rights.spaAcceso abierto
dc.description.additionalLínea de investigación Diseñó Experimental
dc.type.driverinfo:eu-repo/semantics/other
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programBogotá - Ciencias - Maestría en Ciencias - Estadística
dc.contributor.corporatenameUniversidad Nacional de Colombia
dc.description.degreelevelMaestría
dc.publisher.departmentDepartamento de Estadística
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
dc.relation.referencesAntman, F. & McKenzie, D. J. (2007), ‘Earnings mobility and measurement error: Apseudo-panel approach’,Economic Development and Cultural Change56(1), 125–161.
dc.relation.referencesBates, D., M ̈achler, M., Bolker, B. & Walker, S. (2015), ‘Fitting linear mixed-effects modelsusing lme4’,Journal of Statistical Software67(1), 1–48.
dc.relation.referencesCanavire-Bacarreza, G. & Robles, M. (2017), `Non-parametric analysis of poverty duration using repeated cross section: an application for Peru', Applied Economics 49(22), 2141{ 2152.
dc.relation.referencesCarmona, F. (2005), `Modelos lineales', Publicaci on Universidad de Barcelona, Barcelona .
dc.relation.referencesDavis, C. S. (2002), Statistical methods for the analysis of repeated measurements, Springer Science and Business Media, New York.
dc.relation.referencesDeaton, A. (1985), `Panel data from time series of cross-sections', Journal of Econometrics 30(1-2), 109{126.
dc.relation.referencesFaraway, J. J. (2016), Extending the linear model with R: generalized linear, mixed e ects and nonparametric regression models, Chapman and Hall/CRC, Boca Raton.
dc.relation.referencesFederer, W. T. & King, F. (2007), Variations on split plot and split block experiment designs, Vol. 654, John Wiley and Sons, Hoboken.
dc.relation.referencesFinch, W. H., Bolin, J. E. & Kelley, K. (2016), Multilevel modeling using R, Chapman and Hall/CRC, Boca Raton.
dc.relation.referencesGa lecki, A. & Burzykowski, T. (2013), Linear mixed-e ects models using R: A step-by-step approach, Springer Science and Business Media, New York.
dc.relation.referencesGardes, F., Duncan, G. J., Gaubert, P., Gurgand, M. & Starzec, C. (2005), `Panel and pseudo-panel estimation of cross-sectional and time series elasticities of food consumption: The case of US and polish data', Journal of Business and Economic Statistics 23(2), 242{253.
dc.relation.referencesGentle, J. E. (2012), Numerical linear algebra for applications in statistics, Springer Science and Business Media, New York.
dc.relation.referencesGenz, A., Bretz, F., Miwa, T., Mi, X., Leisch, F., Scheipl, F. & Hothorn, T. (2016), mvtnorm: Multivariate Normal and t Distributions. R package version 1.0-5. URL: http://CRAN.R-project.org/package=mvtnorm
dc.relation.referencesHimaz, R. & Aturupane, H. (2016), `Returns to education in Sri Lanka: a pseudo-panel approach', Education Economics 24(3), 300{311.
dc.relation.referencesHinkelmann, K. (2011), Design and analysis of experiments, special designs and applications, Vol. 3, John Wiley and Sons, Blacksburg.
dc.relation.referencesMinTic (2020), `Datos abiertos Colombia', urlhttps://www.datos.gov.co. Accedido 01-08- 2019.
dc.relation.referencesMonroy, L. G. D. & Rivera, M. A. M. (2012), Análisis estadístico de datos multivariados, Universidad Nacional de Colombia, Bogotá.
dc.relation.referencesMontgomery, D. C. (2017), Design and analysis of experiments, John Wiley and Sons, New York.
dc.relation.referencesMelo, O., López, L. & Melo, S. (2007), `Diseño de experimentos: métodos y aplicaciones', Editorial Universidad Nacional de Colombia. Bogotá.
dc.relation.referencesMontgomery, D. C., Peck, E. A. & Vining, G. G. (2012), Introduction to Linear Regression Analysis, Vol. 821, John Wiley and Sons, New York.
dc.relation.referencesPinheiro, J. & Bates, D. (2006), Mixed-e ects models in S and S-PLUS, Springer Science & Business Media, New York.
dc.relation.referencesPinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team (2018), nlme: Linear and Nonlinear Mixed E ects Models. R package version 3.1-131. URL: https://CRAN.R-project.org/package=nlme
dc.relation.referencesPropper, C., Rees, H. & Green, K. (2001), `The demand for private medical insurance in the UK: a cohort analysis', The Economic Journal 111(471), 180{200.
dc.relation.referencesRencher, A. C. (2003), Methods of multivariate analysis, John Wiley and Sons, Provo.
dc.relation.referencesRizzo, M. L. (2007), Statistical computing with R, Chapman and Hall/CRC, Bowling Green.
dc.relation.referencesSchabenberger, O. & Gotway, C. A. (2017), Statistical methods for spatial data analysis, Chapman and Hall/CRC, Boca Raton.
dc.relation.referencesSprietsma, M. (2012), `Computers as pedagogical tools in Brazil: a pseudo-panel analysis', Education Economics 20(1), 19{32.
dc.relation.referencesTovar, A. O., Zulaica, I. G. & N u~nez-Ant on, V. (2012), `Analysis of pseudo-panel data with dependent samples', Journal of Applied Statistics 39(9), 1921{1937.
dc.relation.referencesTsai, C.-H., Mulley, C. & Clifton, G. (2014), `A review of pseudo panel data approach in estimating short-run and long-run public transport demand elasticities', Transport Reviews 34(1), 102{121.
dc.relation.referencesUrdinola, B. P. & Ospino, C. (2015), `Long-term consequences of adolescent fertility: The colombian case', Demographic Research 32, 1487{1518.
dc.relation.referencesVerbeek, M. (2008), Pseudo-panels and repeated cross-sections, in `The econometrics of panel data', Springer, pp. 369{383.
dc.relation.referencesVerbeek, M. & Nijman, T. (1993), `Minimum mse estimation of a regression model with xed e ects from a series of cross-sections', Journal of Econometrics 59(1-2), 125{136.
dc.relation.referencesWei, W. W. (2006), Time series analysis, in `The Oxford Handbook of Quantitative Methods in Psychology: Vol. 2'.
dc.relation.referencesWest, B. T., Welch, K. B. & Galecki, A. T. (2014), Linear mixed models: a practical guide using Statistical Software, CRC Press, Boca Raton.
dc.relation.referencesWickham, H. (2016), ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag, New York. URL: http://ggplot2.org
dc.relation.referencesWickham, H., Fran cois, R., Henry, L. & M uller, K. (2019), dplyr: A Grammar of Data Manipulation. R package version 0.8.3. URL: https://CRAN.R-project.org/package=dplyr
dc.relation.referencesWickham, H. & Henry, L. (2018), tidyr: Easily Tidy Data with 'spread()' and 'gather()' Functions. R package version 0.8.1. URL: https://CRAN.R-project.org/package=tidyr
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.proposalmuestreo destructivo
dc.subject.proposaldestructive sampling
dc.subject.proposalunidades observacionales y experimentales
dc.subject.proposalobservational and experimental units
dc.subject.proposalefectos fijos
dc.subject.proposalmixed effects
dc.subject.proposalefectos mixtos
dc.subject.proposalmultivariate analysis of variance
dc.subject.proposalefectos mixtos
dc.subject.proposalmean square error
dc.subject.proposallongitudinal data
dc.subject.proposalanálisis de varianza multivariado
dc.subject.proposalcuadrado medio del error
dc.subject.proposaldatos longitudinales
dc.type.coarhttp://purl.org/coar/resource_type/c_1843
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2


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Atribución-SinDerivadas 4.0 InternacionalThis work is licensed under a Creative Commons Reconocimiento-NoComercial 4.0.This document has been deposited by the author (s) under the following certificate of deposit