Treffer: Goodness-of-fit tests based on Rao's divergence under sparseness assumptions
Title:
Goodness-of-fit tests based on Rao's divergence under sparseness assumptions
Authors:
Source:
Applied mathematics and computation. 130(2-3):265-283
Publisher Information:
New York, NY: Elsevier, 2002.
Publication Year:
2002
Physical Description:
print, 21 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, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Méthodes numériques en programmation mathématique, optimisation et calcul variationnel, Numerical methods in mathematical programming, optimization and calculus of variations, Programmation mathématique numérique, Numerical methods in mathematical programming, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Loi probabilité, Probability distribution, Ley probabilidad, Méthode statistique, Statistical method, Método estadístico, Normalité asymptotique, Asymptotic normality, Normalidad asintótica, Test ajustement, Goodness of fit test, Prueba ajuste, Fonction puissance, Loi asymptotique, Asymptotic distribution
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Statistics and O.R., Faculty of Mathematics, Complutense University of Madrid, 28040 Madrid, Spain
ISSN:
0096-3003
Rights:
Copyright 2002 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.13799936
Database:
PASCAL Archive
Weitere Informationen
In many practical situations the classical (fixed-cells) assumptions to test goodness-of-fit are inappropriate, and we consider an alternative set of assumptions, which we call sparseness assumptions. It is proved that, under general conditions, the proposed family of statistics based on Rao's divergence is asymptotically normal when the sample size n and the number of cells Mn tend to infinity so that n/Mn → v > 0. This result is extended to contiguous alternatives, and subsequently it is possible to find the asymptotically most efficient member of the family.