Result: Simultaneous Tests for Independence Among Components of Random Vector by Step-Down Multiple Comparison Procedure
Title:
Simultaneous Tests for Independence Among Components of Random Vector by Step-Down Multiple Comparison Procedure
Authors:
Source:
Communications in statistics. Simulation and computation. 41(8-10):1998-2005
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 1/4 p
Original Material:
INIST-CNRS
Subject Terms:
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, Lois de probabilités, Distribution theory, Inférence linéaire, régression, Linear inference, regression, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Analyse multivariable, Multivariate analysis, Análisis multivariable, Analyse numérique, Numerical analysis, Análisis numérico, Comparaison multiple, Multiple comparison, Comparación múltiple, Comparaison par paire, Paired comparison, Comparación por pares, Distribution statistique, Statistical distribution, Distribución estadística, Indépendance, Independence, Independencia, Loi normale, Gaussian distribution, Curva Gauss, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode statistique, Statistical method, Método estadístico, Méthode stochastique, Stochastic method, Método estocástico, Rapport vraisemblance, Likelihood ratio, Relación verosimilitud, Régression statistique, Statistical regression, Regresión estadística, Simulation numérique, Numerical simulation, Simulación numérica, Test indépendance, Independence test, Test independencia, Test rapport vraisemblance, Likelihood ratio test, Test razón verosimilitud, Test statistique, Statistical test, Test estadístico, Théorie approximation, Approximation theory, Vecteur aléatoire, Random vector, Vector aléatorio, 62E17, 62J15, 65C05, Closed testing procedure, Modified likelihood ratio, Primary 62H 15, Secondary 62H 10, Step-down multiple comparison procedure, Testing independence
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Mathematical Information Science, Graduate School of Science, Tokyo University of Sciencej, Japan
Department of Mathematical Information Science, Faculty of Science, Tokyo University of Science, Japan
Department of Mathematical Information Science, Faculty of Science, Tokyo University of Science, Japan
ISSN:
0361-0918
Rights:
Copyright 2015 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.26164040
Database:
PASCAL Archive
Further Information
In this article, we consider testing independence among components of random vector in multivariate normal population. For testing independence, we use the modified likelihood ratio test statistic which is improved an approximation to χ2 distribution of the likelihood ratio test statistic. In order to perform simultaneous tests for independence among components of random vector, we use the step-down multiple comparison procedure based on the closed testing procedure proposed by Marcus et al. (1976). Finally, we perform Monte Carlo simulations and present numerical results.