• Quantile Estimation Using Rank...

Treffer: Quantile Estimation Using Ranked Set Samples from a Population with Known Mean

Title:
Quantile Estimation Using Ranked Set Samples from a Population with Known Mean
Authors:
MAHDIZADEH, M, ARGHAMI, N. R
Source:
Communications in statistics. Simulation and computation. 41(8-10):1872-1881
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 1/2 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, Lois de probabilités, Distribution theory, Inférence paramétrique, Parametric inference, Inférence non paramétrique, Nonparametric inference, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Analyse statistique, Statistical analysis, Análisis estadístico, Comportement asymptotique, Asymptotic behavior, Comportamiento asintótico, Distribution statistique, Statistical distribution, Distribución estadística, Echantillonnage, Sampling, Muestreo, Efficacité estimateur, Estimator efficiency, Eficacia estimador, Efficacité relative, Relative efficiency, Eficacia relativa, Estimation moyenne, Mean estimation, Estimación promedio, Estimation non paramétrique, Non parametric estimation, Estimación no paramétrica, Estimation statistique, Statistical estimation, Estimación estadística, Fonction répartition, Distribution function, Función distribución, Méthode mesure, Measurement method, Método medida, Méthode statistique, Statistical method, Método estadístico, Normalité asymptotique, Asymptotic normality, Normalidad asintótica, Quantile, Cuantila, Simulation numérique, Numerical simulation, Simulación numérica, Statistique rang, Rank statistic, Estadística rango, Théorie information, Information theory, Teoría información, 60E05, 62E20, 62F12, 62G20, Estimation paramétrique, Information auxiliaire, Auxiliary information, Loi asymptotique, 62G30, 62G99, Mean-correction, Quantile estimation, Ranked set sampling
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Statistics, School of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran, Islamic Republic of
ISSN:
0361-0918
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=26164029
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
Notes:
Mathematics
Accession Number:
edscal.26164029
Database:
PASCAL Archive

Weitere Informationen

Ranked set sampling (RSS) is a cost-efficient technique for data collection when the units in a population can be easily judgment ranked by any cheap method other than actual measurements. Using auxiliary information in developing statistical procedures for inference about different population characteristics is a well-known approach. In this work, we deal with quantile estimation from a population with known mean when data are obtained according to RSS scheme. Through the simple device of mean-correction (subtract off the sample mean and add on the known population mean), a modified estimator is constructed from the standard quantile estimator. Asymptotic normality of the new estimator and its asymptotic efficiency relative to the original estimator are derived. Simulation results for several underlying distributions show that the proposed estimator is more efficient than the traditional one.