Result: A flexible Bayesian algorithm for sample size calculations in misclassified data
Title:
A flexible Bayesian algorithm for sample size calculations in misclassified data
Authors:
Source:
International Conference on Computational Methods in Sciences and Engineering 2004 (ICCMSE-2004)Applied mathematics and computation. 184(1):86-92
Publisher Information:
New York, NY: Elsevier, 2007.
Publication Year:
2007
Physical Description:
print, 14 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Théorie de l'échantillonnage, sondages statistiques, Sampling theory, sample surveys, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Algorithme, Algorithm, Algoritmo, Analyse numérique, Numerical analysis, Análisis numérico, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Méthode Bayes, Bayes methods, Taille échantillon, Sample size, Tamaño muestra, Mauvaise classification, Bad classification, Ordre optimal, Recouvrement moyenne, Average coverage, Bayesian point of view, Misclassification
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Telecommunications Science and Technology, University of Peloponnese, Karaiskaki Street, Tripolis 22100, Greece
Deportment of Social and Educational Policy. University of Peloponnese, Damaskinou and Kolokotroni Street, Korinthos 20100, Greece
Deportment of Social and Educational Policy. University of Peloponnese, Damaskinou and Kolokotroni Street, Korinthos 20100, Greece
ISSN:
0096-3003
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.18551617
Database:
PASCAL Archive
Further Information
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.