Result: CONSTRUCTION OF COVARIANCE MATRICES WITH A SPECIFIED DISCREPANCY FUNCTION MINIMIZER, WITH APPLICATION TO FACTOR ANALYSIS
Title:
CONSTRUCTION OF COVARIANCE MATRICES WITH A SPECIFIED DISCREPANCY FUNCTION MINIMIZER, WITH APPLICATION TO FACTOR ANALYSIS
Authors:
Source:
SIAM journal on matrix analysis and applications. 31(4):1570-1583
Publisher Information:
Philadelphia, PA: Society for Industrial and Applied Mathematics, 2010.
Publication Year:
2010
Physical Description:
print, 11 ref
Original Material:
INIST-CNRS
Subject Terms:
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, Algèbre, Algebra, Algèbre linéaire et multilinéaire, matrices, Linear and multilinear algebra, matrix theory, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Analyse multivariable, Multivariate analysis, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Algèbre linéaire numérique, Numerical linear algebra, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Algèbre linéaire, Linear algebra, Algebra lineal, Analyse covariance, Covariance analysis, Análisis covariancia, Analyse factorielle, Factor analysis, Análisis factorial, Analyse numérique, Numerical analysis, Análisis numérico, Covariance, Covariancia, Estimation statistique, Statistical estimation, Estimación estadística, Etude statistique, Statistical study, Estudio estadístico, Fonction valeur, Value function, Función valor, Matrice covariance, Covariance matrix, Matriz covariancia, Maximum vraisemblance, Maximum likelihood, Maxima verosimilitud, Modèle structure, Structural model, Modelo estructura, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode moindre carré, Least squares method, Método cuadrado menor, Programmation semi définie, Semi definite programming, Programacíon semi definida, 62E17, 62H25, 62J10, 65C05, Discrépance, Modèle factoriel, Factor model, Modèle mal spécifié, Misspecified model, Moindre carré généralisé, Generalized least square, 90C26, covariance structure analysis, discrepancy function, factor analysis, generalized least squares, maximum likelihood, model misspecification, semidefinite programming
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
Georgia Institute of Technology, Atlanta, Georgia 30332, United States
ISSN:
0895-4798
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.23303063
Database:
PASCAL Archive
Further Information
The main goal of this paper is to develop a numerical procedure for construction of covariance matrices such that for a given covariance structural model and a discrepancy function the corresponding minimizer of the discrepancy function has a specified value. Often construction of such matrices is a first step in Monte Carlo studies of statistical inferences of misspecified models. We analyze theoretical aspects of the problem and suggest a numerical procedure based on semidefinite programming techniques. As an example, we discuss in detail the factor analysis model.