Treffer: The Use of Permutation Tests for Variance Components in Linear Mixed Models : Statistics for Complex Problems: Permutation Testing Methos and Related Topics
Title:
The Use of Permutation Tests for Variance Components in Linear Mixed Models : Statistics for Complex Problems: Permutation Testing Methos and Related Topics
Source:
Communications in statistics. Theory and methods. 41(16-18):3020-3029
Publisher Information:
Philadelphia, PA: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 3/4 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, Généralités, General topics, 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 covariance, Covariance analysis, Análisis covariancia, Analyse numérique, Numerical analysis, Análisis numérico, Analyse variance, Variance analysis, Análisis variancia, Bootstrap, Composante variance, Variance component, Componente variancia, Distribution statistique, Statistical distribution, Distribución estadística, Effet aléatoire, Random effect, Efecto aleatorio, Espace paramètre, Parameter space, Espacio par metro, Khi deux, Chi square, Ji cuadrado, Modèle linéaire, Linear model, Modelo lineal, Modèle mixte, Mixed model, Modelo mixto, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode jackknife, Jackknife method, Método jackknife, Méthode rééchantillonnage, Resampling method, 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 statistique, Statistical simulation, Simulación estadística, Test hypothèse, Hypothesis test, Test hipótesis, Test rapport vraisemblance, Likelihood ratio test, Test razón verosimilitud, Test signification, Significance test, Test significación, Théorie approximation, Approximation theory, 62E17, 62E20, 62F40, 62J10, 65C05, Estimation paramétrique, Loi asymptotique, Niveau signification, Significance level, Statistique score, Score statistics, Test aléatoire, Random test, Test permutation, Permutation test, Bootstrap test, Linear mixed model, Primary 62G09, Secondary 62J12
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Statistical Sciences, University of Padua, Padua, Italy
Department of Statistics, University of Florence, Florence, Italy
Department of Management and Engineering, University of Padua, Italy
Department of Statistics, University of Florence, Florence, Italy
Department of Management and Engineering, University of Padua, Italy
ISSN:
0361-0926
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.26341210
Database:
PASCAL Archive
Weitere Informationen
Standard asymptotic chi-square distribution of the likelihood ratio and score statistics under the null hypothesis does not hold when the parameter value is on the boundary of the parameter space. In mixed models it is of interest to test for a zero random effect variance component. Some available tests for the variance component are reviewed and a new test within the permutation framework is presented. The power and significance level of the different tests are investigated by means of a Monte Carlo simulation study. The proposed test has a significance level closer to the nominal one and it is more powerful.