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.
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Acknowledgments
Thanks to Dr. Paul Neal for analysis of some of the field data. We would also like to thank the editors for helpful comments and suggestions, which have led to an improved version of this chapter.
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Yu, F., Chen, MH., Huang, L., Anderson, G.J. (2013). Hierarchical Bayesian Analysis of Repeated Binary Data with Missing Covariates. In: Hu, M., Liu, Y., Lin, J. (eds) Topics in Applied Statistics. Springer Proceedings in Mathematics & Statistics, vol 55. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7846-1_25
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DOI: https://doi.org/10.1007/978-1-4614-7846-1_25
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