Hierarchical Bayesian Analysis of Repeated Binary Data with Missing Covariates

Fang Yu, Ming Hui Chen, Lan Huang, Gregory J. Anderson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Missing covariates are a common problem in many biomedical and environmental studies. In this chapter, we develop a hierarchical Bayesian method for analyzing data with repeated binary responses over time and time-dependent missing covariates. The fitted model consists of two parts: a generalized linear mixed probit regression model for the repeated binary responses and a joint model to incorporate information from different sources for time-dependent missing covariates. A Gibbs sampling algorithm is developed for carrying out posterior computation. The importance of the covariates is assessed via the deviance information criterion. We revisit the real plant dataset considered by Huang et al. (2008) and use it to illustrate the proposed methodology. The results from the proposed methods are compared with those in Huang et al. (2008). Similar top models and estimates of model parameters are obtained by both methods.

Original languageEnglish (US)
Title of host publicationTopics in Applied Statistics - 2012 Symposium of the International Chinese Statistical Association
Pages311-322
Number of pages12
DOIs
StatePublished - Oct 28 2013
Event21st Symposium of the International Chinese Statistical Association, ICSA 2012 - Boston, MA, United States
Duration: Jun 23 2012Jun 26 2012

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume55
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Conference

Conference21st Symposium of the International Chinese Statistical Association, ICSA 2012
CountryUnited States
CityBoston, MA
Period6/23/126/26/12

Fingerprint

Missing Covariates
Binary Data
Bayesian Analysis
Time-dependent Covariates
Binary Response
Probit Regression
Deviance Information Criterion
Probit Model
Joint Model
Gibbs Sampling
Bayesian Methods
Covariates
Regression Model
Model
Methodology
Estimate

ASJC Scopus subject areas

  • Mathematics(all)

Cite this

Yu, F., Chen, M. H., Huang, L., & Anderson, G. J. (2013). Hierarchical Bayesian Analysis of Repeated Binary Data with Missing Covariates. In Topics in Applied Statistics - 2012 Symposium of the International Chinese Statistical Association (pp. 311-322). (Springer Proceedings in Mathematics and Statistics; Vol. 55). https://doi.org/10.1007/978-1-4614-7846-1_25

Hierarchical Bayesian Analysis of Repeated Binary Data with Missing Covariates. / Yu, Fang; Chen, Ming Hui; Huang, Lan; Anderson, Gregory J.

Topics in Applied Statistics - 2012 Symposium of the International Chinese Statistical Association. 2013. p. 311-322 (Springer Proceedings in Mathematics and Statistics; Vol. 55).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yu, F, Chen, MH, Huang, L & Anderson, GJ 2013, Hierarchical Bayesian Analysis of Repeated Binary Data with Missing Covariates. in Topics in Applied Statistics - 2012 Symposium of the International Chinese Statistical Association. Springer Proceedings in Mathematics and Statistics, vol. 55, pp. 311-322, 21st Symposium of the International Chinese Statistical Association, ICSA 2012, Boston, MA, United States, 6/23/12. https://doi.org/10.1007/978-1-4614-7846-1_25
Yu F, Chen MH, Huang L, Anderson GJ. Hierarchical Bayesian Analysis of Repeated Binary Data with Missing Covariates. In Topics in Applied Statistics - 2012 Symposium of the International Chinese Statistical Association. 2013. p. 311-322. (Springer Proceedings in Mathematics and Statistics). https://doi.org/10.1007/978-1-4614-7846-1_25
Yu, Fang ; Chen, Ming Hui ; Huang, Lan ; Anderson, Gregory J. / Hierarchical Bayesian Analysis of Repeated Binary Data with Missing Covariates. Topics in Applied Statistics - 2012 Symposium of the International Chinese Statistical Association. 2013. pp. 311-322 (Springer Proceedings in Mathematics and Statistics).
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