A flexible Bayesian algorithm for sample size calculations in misclassified data

被引：5
|
作者：
Nistazakis, Hector E.
Katsis, Athanassios
机构：
[1] Univ Peloponnese, Dept Social & Educ Policy, Korinthos 20100, Greece
[2] Univ Peloponnese, Dept Telecommun Sci & Technol, Tripolis 22100, Greece
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2007年 / 184卷 / 01期
关键词：
sample size; misclassification; Bayesian point of view; average coverage;
D O I：
10.1016/j.amc.2005.12.071
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
The problem of obtaining a flexible and easy to implement algorithm in order to derive the optimal sample size when the data are subject to misclassification is critical to practitioners. The topic is addressed from the Bayesian point of view where a special structure of the a priori parameter information is investigated. The proposed methodology is applied in specific examples. (c) 2006 Elsevier Inc. All rights reserved.
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页码：86 / 92
页数：7
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