JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Inferential Problems in Bayesian Logistic Regression Models
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Inferential Problems in Bayesian Logistic Regression Models
Hwang, Jin-Soo; Kang, Sung-Chan;
  PDF(new window)
 Abstract
Model selection and hypothesis testing problems in Bayesian inference are still debated between scholars. Bayesian factors traditionally used as a criterion in Bayesian hypothesis testing and model selection, are easy to understand but sometimes hard to compute. In addition, there are other model selection criterions such as DIC(Deviance Information Criterion) by Spiegelhalter et al. (2002) and Bayesian P-values for testing. In this paper, we briefly introduce the Bayesian hypothesis testing and model selection procedure. In addition we have applied a Bayesian inference to Swiss banknote data by a fitting logistic regression model and computing several test statistics to see if they provide consistent results.
 Keywords
Bayesian Model Selection;Bayes factor;DIC;Bayesian P-value;
 Language
Korean
 Cited by
1.
베이지안 추정을 이용한 팔당호 유역의 계절별 클로로필a 예측 및 오염특성 연구,김미아;신유나;김경현;허태영;유문규;이수웅;

한국물환경학회지, 2013. vol.29. 6, pp.832-841
 References
1.
김달호 (2004). , 자유아카데미.

2.
Bayarri, M. J. and Berger, J. (1998). P-values for Composite Null Models, ISDS Discussion Paper, 98-40, Duke University.

3.
Box, G. E. P. (1980). Sampling and Bayes inference in scienti c modeling and robustness, Journal of Royal Statistical Society, Series A, 143, 383-430. crossref(new window)

4.
Flurry, B. and Riedwyl, H. (1988). Multivariate Statistics: A Practical Approach, Chapman and Hall.

5.
Gelman, A., Meng, X. L. and Stern, H. S. (1996). Posterior predictive assessment of model finess via realized discrepancies (with discussion), Statistica Sinica, 6, 733-807.

6.
Jeffreys, H. (1961). Theory of Probability, 3rd Ed. Oxford University Press, New York.

7.
Kass, R. E. and Raftery, A. E. (1995). Bayes factors, Journal of American Statistical Association, 90, 773-795. crossref(new window)

8.
Marin, J. M. and Robert, C. P. (2007). Bayesian Core: A practical Approach to Computational Bayesian Statistics, Springer.

9.
Meng, X.-L. (1994). Posterior predictive p-values, Annals of Statistics, 22, 1142-1160. crossref(new window)

10.
Rubin, D. B. (1984). Bayesianly justi able and relevant frequency calculations for the applied statistician, Annals of Statistics, 12, 1151-1172. crossref(new window)

11.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P. and van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion), Journal of the Royal Statistical Society: Series B, 64, 583-639. crossref(new window)