Publicado

2022-07-14

Some Developments in Bayesian Hierarchical Linear Regression Modeling

Algunos desarrollos en modelos de regresión lineal jerárquicos bayesianos

DOI:

https://doi.org/10.15446/rce.v45n2.98988

Palabras clave:

Bayesian Inference, Clustering, Gibbs Sampling, Hierarchical Model, Linear Regression (en)
Agrupamiento, Inferencia bayesiana, Muestreador de Gibb, Modelo jerárquico, Regresión lineal (es)

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Autores/as

  • Juan Sosa 1Departamento de Estadistíca, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá, Colombia
  • Jeimy-Paola Aristizabal Facultad de Administración de Empresas, Universidad Externado de Colombia, Bogotá, Colombia

Considering the flexibility and applicability of Bayesian modeling, in this work we revise the main characteristics of two hierarchical models in a regression setting. We study the full probabilistic structure of the models along with the full conditional distribution for each model parameter. Under our hierarchical extensions, we allow the mean of the second stage of the model to have a linear dependency on a set of covariates. The Gibbs sampling algorithms used to obtain samples when fitting the models are fully described and derived. In addition, we consider a case study in which the plant size is characterized as a function of nitrogen soil concentration and a grouping factor (farm).

Considerando la exibilidad y aplicabilidad del modelamiento Bayesiano, en este trabajo se revisan las principales características de dos modelos jerárquicos en un escenario de regresión. Se estudia la estructura probabilística completa de los modelos junto con la distribución condicional completa para cada parámetro del modelo. Las extensiones jerárquicas que se presentan permiten que la media de la segunda etapa del modelo tenga una dependencia lineal de un conjunto de covariables. Se describen y derivan completamente los algoritmos de muestreo de Gibbs para ajustar los modelos. Además, se considera un caso de estudio en el que se caracteriza el tamaño de plantas en función de la concentración de nitrógeno en el suelo y un factor de agrupación (fincas).

Referencias

Albert, J. (2009), Bayesian Computation with R, Use R!, Springer. DOI: https://doi.org/10.1007/978-0-387-92298-0

Christensen, R., Johnson, W., Branscum, A. & Hanson, T. (2011), Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians, Chapman & Hall/CRC Texts in Statistical Science, Taylor & Francis. http://books.google.com/books?id=qPERhCbePNcC

Ferguson, T. (1973), 'A bayesian analysis of some nonparametric problems', The annals of statistics pp. 209-230. DOI: https://doi.org/10.1214/aos/1176342360

Gamerman, D. & Lopes, H. (2006), Markov chain Monte Carlo: stochastic simulation for Bayesian inference, CRC Press. DOI: https://doi.org/10.1201/9781482296426

Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A. & Rubin, D. (2013), Bayesian Data Analysis, Third Edition, Chapman & Hall/CRC Texts in Statistical Science, Taylor & Francis. DOI: https://doi.org/10.1201/b16018

Gelman, A., Hwang, J. & Vehtari, A. (2014), 'Understanding predictive information criteria for bayesian models', Statistics and computing 24(6), 997-1016. DOI: https://doi.org/10.1007/s11222-013-9416-2

Hoff P. (2009), A First Course in Bayesian Statistical Methods, Springer Texts in Statistics, Springer. DOI: https://doi.org/10.1007/978-0-387-92407-6

Ishwaran, H. & Zarepour, M. (2000), 'Markov chain monte carlo in approximate dirichlet and beta two-parameter process hierarchical models', Biometrika 87(2), 371-390. DOI: https://doi.org/10.1093/biomet/87.2.371

Kass, R. & Wasserman, L. (1995), 'A reference bayesian test for nested hypotheses and its relationship to the schwarz criterion', Journal of the american statistical association, 90(431), 928-934. DOI: https://doi.org/10.1080/01621459.1995.10476592

Lau, J. & Green, P. (2007), 'Bayesian model-based clustering procedures', Journal of Computational and Graphical Statistics 16(3), 526-558. DOI: https://doi.org/10.1198/106186007X238855

Müller, P., Quintana, F. A., Jara, A. & Hanson, T. (2015), Bayesian nonparametric data analysis, Springer. DOI: https://doi.org/10.1007/978-3-319-18968-0

Pitman, J. & Yor, M. (1997), 'The two-parameter poisson-dirichlet distribution derived from a stable subordinator', The Annals of Probability pp. 855-900. DOI: https://doi.org/10.1214/aop/1024404422

Sethuraman, J. (1994), 'A constructive definition of dirichlet priors', Statistica sinica pp. 639-650.

Spiegelhalter, D. J., Best, N. G., Carlin, B. P. & Linde, A. (2014), 'The deviance information criterion: 12 years on', Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76(3), 485-493. DOI: https://doi.org/10.1111/rssb.12062

Spiegelhalter, D. J., Best, N. G., Carlin, B. P. & Van Der Linde, A. (2002), 'Bayesian measures of model complexity and fit', Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583-639. DOI: https://doi.org/10.1111/1467-9868.00353

Stephens, M. (2000), 'Dealing with label switching in mixture models', Journal of the Royal Statistical Society: Series B (Statistical Methodology) 62(4), 795-809. DOI: https://doi.org/10.1111/1467-9868.00265

Watanabe, S. (2010), 'Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory', Journal of Machine Learning Research 11(Dec), 3571-3594.

Watanabe, S. (2013), 'A widely applicable Bayesian information criterion', Journal of Machine Learning Research 14(Mar), 867-897.

Cómo citar

APA

Sosa, J. y Aristizabal, J.-P. (2022). Some Developments in Bayesian Hierarchical Linear Regression Modeling. Revista Colombiana de Estadística, 45(2), 231–255. https://doi.org/10.15446/rce.v45n2.98988

ACM

[1]
Sosa, J. y Aristizabal, J.-P. 2022. Some Developments in Bayesian Hierarchical Linear Regression Modeling. Revista Colombiana de Estadística. 45, 2 (jul. 2022), 231–255. DOI:https://doi.org/10.15446/rce.v45n2.98988.

ACS

(1)
Sosa, J.; Aristizabal, J.-P. Some Developments in Bayesian Hierarchical Linear Regression Modeling. Rev. colomb. estad. 2022, 45, 231-255.

ABNT

SOSA, J.; ARISTIZABAL, J.-P. Some Developments in Bayesian Hierarchical Linear Regression Modeling. Revista Colombiana de Estadística, [S. l.], v. 45, n. 2, p. 231–255, 2022. DOI: 10.15446/rce.v45n2.98988. Disponível em: https://revistas.unal.edu.co/index.php/estad/article/view/98988. Acesso em: 1 abr. 2025.

Chicago

Sosa, Juan, y Jeimy-Paola Aristizabal. 2022. «Some Developments in Bayesian Hierarchical Linear Regression Modeling». Revista Colombiana De Estadística 45 (2):231-55. https://doi.org/10.15446/rce.v45n2.98988.

Harvard

Sosa, J. y Aristizabal, J.-P. (2022) «Some Developments in Bayesian Hierarchical Linear Regression Modeling», Revista Colombiana de Estadística, 45(2), pp. 231–255. doi: 10.15446/rce.v45n2.98988.

IEEE

[1]
J. Sosa y J.-P. Aristizabal, «Some Developments in Bayesian Hierarchical Linear Regression Modeling», Rev. colomb. estad., vol. 45, n.º 2, pp. 231–255, jul. 2022.

MLA

Sosa, J., y J.-P. Aristizabal. «Some Developments in Bayesian Hierarchical Linear Regression Modeling». Revista Colombiana de Estadística, vol. 45, n.º 2, julio de 2022, pp. 231-55, doi:10.15446/rce.v45n2.98988.

Turabian

Sosa, Juan, y Jeimy-Paola Aristizabal. «Some Developments in Bayesian Hierarchical Linear Regression Modeling». Revista Colombiana de Estadística 45, no. 2 (julio 14, 2022): 231–255. Accedido abril 1, 2025. https://revistas.unal.edu.co/index.php/estad/article/view/98988.

Vancouver

1.
Sosa J, Aristizabal J-P. Some Developments in Bayesian Hierarchical Linear Regression Modeling. Rev. colomb. estad. [Internet]. 14 de julio de 2022 [citado 1 de abril de 2025];45(2):231-55. Disponible en: https://revistas.unal.edu.co/index.php/estad/article/view/98988

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