TY - GEN
T1 - Hierarchical Bayesian Analysis of Repeated Binary Data with Missing Covariates
AU - Yu, Fang
AU - Chen, Ming Hui
AU - Huang, Lan
AU - Anderson, Gregory J.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84885990391&partnerID=8YFLogxK
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U2 - 10.1007/978-1-4614-7846-1_25
DO - 10.1007/978-1-4614-7846-1_25
M3 - Conference contribution
AN - SCOPUS:84885990391
SN - 9781461478454
T3 - Springer Proceedings in Mathematics and Statistics
SP - 311
EP - 322
BT - Topics in Applied Statistics - 2012 Symposium of the International Chinese Statistical Association
T2 - 21st Symposium of the International Chinese Statistical Association, ICSA 2012
Y2 - 23 June 2012 through 26 June 2012
ER -