Result: New Results on the Small-Sample Properties of Some Robust Univariate Estimators of Location
Title:
New Results on the Small-Sample Properties of Some Robust Univariate Estimators of Location
Authors:
Source:
Communications in statistics. Simulation and computation. 41(8-10):1544-1556
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 1 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, Inférence paramétrique, Parametric inference, Inférence non paramétrique, Nonparametric inference, Analyse multivariable, Multivariate analysis, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Analyse discriminante, Discriminant analysis, Análisis discriminante, Analyse multivariable, Multivariate analysis, Análisis multivariable, Distribution statistique, Statistical distribution, Distribución estadística, Efficacité estimateur, Estimator efficiency, Eficacia estimador, Efficacité relative, Relative efficiency, Eficacia relativa, Estimation M, M estimation, Estimación M, Estimation moyenne, Mean estimation, Estimación promedio, Estimation non paramétrique, Non parametric estimation, Estimación no paramétrica, Estimation ponctuelle, Point estimation, Estimación puntual, Estimation statistique, Statistical estimation, Estimación estadística, Méthode statistique, Statistical method, Método estadístico, Observation aberrante, Outlier, Observación aberrante, Robustesse estimateur, Estimator robustness, Robustez estimador, Simulation numérique, Numerical simulation, Simulación numérica, Test hypothèse, Hypothesis test, Test hipótesis, Test statistique, Statistical test, Test estadístico, Théorie approximation, Approximation theory, 62E17, 62F03, 62F10, 62G10, 62H15, 62H30, Classification automatique(statistiques), Erreur type I, Error type I, Estimation paramétrique, Moyenne tronquée, Trimmed mean, Propriété petit échantillon, Small sample property, Efficiency, M-estimator, Modified one-step, One-step M-estimator, Primary 62F35, Real data, Secondary 62G05, Tau estimator, rfch estimator, tbs estimator
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Statistics, Dokuz Eylül University, Izmir, Turkey
Department of Psychology, University of Southern California, Los Angeles, California, United States
Department of Psychology, University of Southern California, Los Angeles, California, United States
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.26164006
Database:
PASCAL Archive
Further Information
This article compares eight estimators in terms of relative efficiencies with the univariate mean, some of which have not been compared previously. Four estimators, when testing hypotheses, are compared in terms of actual Type I errors. In terms of point estimation, the modified one-step M-estimator, one-step M-estimator, and rfch estimator are found to be the three best choices depending on the proportion of outliers. In terms of actual Type I errors, the modified one-step M estitnator's and rfch estimator's level was between .045 and .055 in 5 out of 7 situations when real data were used in simulations.