Browse > Article
http://dx.doi.org/10.7465/jkdi.2014.25.1.187

Sensitivity analysis in Bayesian nonignorable selection model for binary responses  

Choi, Seong Mi (Department of Statistics, Kyungpook National University)
Kim, Dal Ho (Department of Statistics, Kyungpook National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.25, no.1, 2014 , pp. 187-194 More about this Journal
Abstract
We consider a Bayesian nonignorable selection model to accommodate the selection bias. Markov chain Monte Carlo methods is known to be very useful to fit the nonignorable selection model. However, sensitivity to prior assumptions on parameters for selection mechanism is a potential problem. To quantify the sensitivity to prior assumption, the deviance information criterion and the conditional predictive ordinate are used to compare the goodness-of-fit under two different prior specifications. It turns out that the 'MLE' prior gives better fit than the 'uniform' prior in viewpoints of goodness-of-fit measures.
Keywords
Bayesian; binary; CPO; DIC; goodness-of-fit; MCMC; nonignorable; selection bias; sensitivity;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Carlin, B. P. and Louis, T. A. (2009). Bayesian methods for data analysis, 3rd Ed., Chapman & Hall/CRC, Boca Raton.
2 Gelfand, A. E., Dey, D. K. and Chang, H. (1992). Model determination using predictive distributions with implementation via sampling-based method (with discussion). In Bayesian Statistics 4, edited by J.M. Bernardo, et al., Oxford University Perss, Oxford, 147-167.
3 Gelman, A. (2006). Prior distribution for variance parameters in hierarchical models. Bayesian Analysis, 1, 515-533.   DOI
4 Gneiting, T. and Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American Statistical Association, 102, 359-378.   DOI   ScienceOn
5 Kwak, S. and Kim, D. (2012). Bayesian estimation for nite population proportions in multinomial data. Journal of the Korean Data & Information Science Society, 23, 587-593.   DOI   ScienceOn
6 Malec, D., Davis, W. W. and Cao, X. (1999). Model-based small area estimates of overweight prevalence using sample selection adjustment. Statistics in Medicine, 18, 3189-3200.   DOI
7 Nandram, B., Bhatta, D., Bhadra, D. and Shen, G. (2013). Bayesian predictive inference of a nite population proportion under selection bias. Statistical Methodology, 11, 1-21.   DOI   ScienceOn
8 Nandram, B. and Choi, J. W. (2010). A Bayesian analysis of body mass index data from small domains under nonignorable nonresponse and selection. Journal of the American Statistical Association, 105, 120-135.   DOI   ScienceOn
9 Roos, M. and Held, L. (2011), Sensitivity analysis in Bayesian generalized linear mixed models for binary data. Bayesian Analysis, 6, 259-278.   DOI
10 Spiegelhalter, D., Best, N., Carlin, B. and van der Linde, A. (2002). Bayesian measure of model complexity and fit. Journal of the Royal Statistical Society B, 64, 583-616.   DOI   ScienceOn