Result: On the Iteratively Reweighted Rank Regression Estimator
Title:
On the Iteratively Reweighted Rank Regression Estimator
Authors:
Source:
Communications in statistics. Simulation and computation. 41(1-2):155-166
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 1/4 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, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, 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, Coefficient régression, Regression coefficient, Coeficiente regresión, Efficacité relative, Relative efficiency, Eficacia relativa, Estimation erreur, Error estimation, Estimación error, Fonction influence, Influence function, Función influencia, Méthode moindre carré, Least squares method, Método cuadrado menor, Méthode statistique, Statistical method, Método estadístico, Queue distribution, Distribution tail, Cola distribución, Queue lourde, Heavy tail, Cola pesada, Régression statistique, Statistical regression, Regresión estadística, Simulation numérique, Numerical simulation, Simulación numérica, Statistique rang, Rank statistic, Estadística rango, 49K40, 62Jxx, Echantillon fini, Finite sample, Fonction bornée, IRLS, Primary 62J05, Rank regression, Secondary 62G99, Sensitivity curve, Wilcoxon norm
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Mathematics and Statistics, Auburn University, Auburn, Alabama, 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.25576918
Database:
PASCAL Archive
Further Information
The finite sample performance of the rank estimator of regression coefficients obtained using the iteratively reweighted least squares (IRLS) of Sievers and Abebe (2004) is evaluated. Efficiency comparisons show that the IRLS method does quite well in comparison to least squares or the traditional rank estimates in cases of moderate-tailed error distributions; however, the IRLS method does not appear to be suitable for heavy-tailed data. Moreover, our results show that the IRLS estimator will have an unbounded influence function even if we use an initial estimator with a bounded influence function.