A flexible Bayesian algorithm for sample size calculations in misclassified data

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 infor...

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Bibliographic Details
Published inApplied mathematics and computation Vol. 184; no. 1; pp. 86 - 92
Main Authors Nistazakis, Hector E., Katsis, Athanassios
Format Journal Article Conference Proceeding
LanguageEnglish
Published New York, NY Elsevier Inc 2007
Elsevier
Subjects
Average coverage
Bayesian point of view
Exact sciences and technology
Mathematics
Misclassification
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Probability and statistics
Sample size
Sampling theory, sample surveys
Sciences and techniques of general use
Statistics
Average coverage
Misclassification
Sample size
Bayesian point of view
Numerical analysis
Applied mathematics
Bad classification
Bayes methods
Algorithm
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Summary: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.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2005.12.071