Bayesian Subset Selection of Binomial Parameters Using Possibly Misclassified Data

被引：0

作者：

Stamey, James D. ^{[1
]}

Bratcher, Thomas L. ^{[2
]}

Young, Dean M. ^{[3
]}

机构：

[1] Baylor Univ, Dept Stat Sci, Waco, TX 76798 USA

[2] Baylor Univ, Waco, TX 76798 USA

[3] Baylor Univ, Stat, Waco, TX 76798 USA

来源：

JOURNAL OF MODERN APPLIED STATISTICAL METHODS | 2007年 / 6卷 / 02期

关键词：

Bayes; posterior approximation; Gibbs Sampler; binomial parameter subset selection;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Three Bayesian approaches are considered for the selection of binomial proportion parameters when data is subject to misclassification. The cases where the misclassification is non-differential and differential were considered, thus extending previous work which considered only non-differential misclassification. In this article, various selection criteria are applied to a simulated data set and a real data set.

引用

页码：551 / 560

页数：10

共 22 条

[1] A BAYESIAN-APPROACH TO RANKING AND SELECTION OF RELATED MEANS WITH ALTERNATIVES TO ANALYSIS-OF-VARIANCE METHODOLOGY [J].