Journal of Applied Mathematics

Bayesian Analysis for Dynamic Generalized Linear Latent Model with Application to Tree Survival Rate

Yu-sheng Cheng, Mei-wen Ding, Ye-mao Xia, and Wen-fa Zhan

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Abstract

Logistic regression model is the most popular regression technique, available for modeling categorical data especially for dichotomous variables. Classic logistic regression model is typically used to interpret relationship between response variables and explanatory variables. However, in real applications, most data sets are collected in follow-up, which leads to the temporal correlation among the data. In order to characterize the different variables correlations, a new method about the latent variables is introduced in this study. At the same time, the latent variables about AR (1) model are used to depict time dependence. In the framework of Bayesian analysis, parameters estimates and statistical inferences are carried out via Gibbs sampler with Metropolis-Hastings (MH) algorithm. Model comparison, based on the Bayes factor, and forecasting/smoothing of the survival rate of the tree are established. A simulation study is conducted to assess the performance of the proposed method and a pika data set is analyzed to illustrate the real application. Since Bayes factor approaches vary significantly, efficiency tests have been performed in order to decide which solution provides a better tool for the analysis of real relational data sets.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 783494, 8 pages.

Dates
First available in Project Euclid: 2 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425305879

Digital Object Identifier
doi:10.1155/2014/783494

Citation

Cheng, Yu-sheng; Ding, Mei-wen; Xia, Ye-mao; Zhan, Wen-fa. Bayesian Analysis for Dynamic Generalized Linear Latent Model with Application to Tree Survival Rate. J. Appl. Math. 2014 (2014), Article ID 783494, 8 pages. doi:10.1155/2014/783494. https://projecteuclid.org/euclid.jam/1425305879


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