Treffer: Monte Carlo tests with nuisance parameters : A general approach to finite-sample inference and nonstandard asymptotics

Title:
Monte Carlo tests with nuisance parameters : A general approach to finite-sample inference and nonstandard asymptotics
Authors:
DUFOUR, Jean-Marie
Source:
Annals journal of econometrics: Resampling methods in econometricsJournal of econometrics. 133(2):443-477
Publisher Information:
Amsterdam: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 2 p.1/2
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, 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, Inférence à partir de processus stochastiques; analyse des séries temporelles, Inference from stochastic processes; time series analysis, Applications, Assurances, économie, finance, Insurance, economics, finance, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Bootstrap, Borne inférieure, Lower bound, Cota inferior, Borne supérieure, Upper bound, Cota superior, Donnée économique, Economic data, Dato económico, Econométrie, Econometrics, Econometría, Estimation statistique, Statistical estimation, Estimación estadística, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode paramétrique, Parametric method, Método paramétrico, Méthode statistique, Statistical method, Método estadístico, Paramètre nuisance, Nuisance parameter, Parámetro daño, Recuit simulé, Simulated annealing, Recocido simulado, Test hypothèse, Hypothesis test, Test hipótesis, Echantillon fini, Finite sample, Loi asymptotique, Test exact, Exact test, C12;C15; C2; C52; C22, Monte Carlo test; Maximized Monte Carlo test; Finite-sample test; Exact test; Nuisance parameter; Bounds; Bootstrap; Parametric bootstrap; Simulated annealing; Asymptotics; Nonstandard asymptotic distribution
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Département de sciences économiques, Université de Montréal, C.P. 6128 succursale Centre-ville, Montréal, Québec, H3C 3J7, Canada
ISSN:
0304-4076
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18088585
Rights:
Copyright 2006 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.18088585
Database:
PASCAL Archive

Weitere Informationen

The technique of Monte Carlo (MC) tests [Dwass (1957, Annals of Mathematical Statistics 28, 181-187); Barnard (1963, Journal of the Royal Statistical Society, Series B 25, 294)] provides a simple method for building exact tests from statistics whose finite sample distribution is intractable but can be simulated (when no nuisance parameter is involved). We extend this method in two ways: first, by allowing for MC tests based on exchangeable possibly discrete test statistics; second, by generalizing it to statistics whose null distribution involves nuisance parameters [maximized MC (MMC) tests]. Simplified asymptotically justified versions of the MMC method are also proposed: these provide a simple way of improving standard asymptotics and dealing with nonstandard asymptotics.