Some Developments in Bayesian Hierarchical Linear Regression Modeling
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| Natura: | Artículo científico |
| Lingua: | en |
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Universidad Nacional de Colombia
2022
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| _version_ | 1876465003978031104 |
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| author | Juan Sosa |
| author_facet | Juan Sosa |
| contents | Some Developments in Bayesian Hierarchical Linear Regression Modeling Juan Sosa Jeimy-Paola Aristizabal Física, Astronomía y Matemáticas Clustering Gibbs Sampling Linear regression Bayesian inference Hierarchical model 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). 2022 artículo científico 0120-1751 https://www.redalyc.org/articulo.oa?id=89981229005 en http://www.redalyc.org/revista.oa?id=899 Revista Colombiana de Estadística application/pdf Universidad Nacional de Colombia Revista Colombiana de Estadística (Colombia) Num.2 Vol.45 |
| format | Artículo científico |
| id | redalyc_89981229005 |
| institution | Redalyc |
| language | en |
| publishDate | 2022 |
| publisher | Universidad Nacional de Colombia |
| spellingShingle | Some Developments in Bayesian Hierarchical Linear Regression Modeling Juan Sosa Física, Astronomía y Matemáticas Clustering Gibbs Sampling Linear regression Bayesian inference Hierarchical model Some Developments in Bayesian Hierarchical Linear Regression Modeling Juan Sosa Jeimy-Paola Aristizabal Física, Astronomía y Matemáticas Clustering Gibbs Sampling Linear regression Bayesian inference Hierarchical model 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). 2022 artículo científico 0120-1751 https://www.redalyc.org/articulo.oa?id=89981229005 en http://www.redalyc.org/revista.oa?id=899 Revista Colombiana de Estadística application/pdf Universidad Nacional de Colombia Revista Colombiana de Estadística (Colombia) Num.2 Vol.45 |
| title | Some Developments in Bayesian Hierarchical Linear Regression Modeling |
| topic | Física, Astronomía y Matemáticas Clustering Gibbs Sampling Linear regression Bayesian inference Hierarchical model |
| url | https://www.redalyc.org/articulo.oa?id=89981229005 |