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Title:Finite Population Quantile Estimators
Author(s):Georgescu, Constantin
Doctoral Committee Chair(s):Stephen Portnoy
Department / Program:Statistics
Discipline:Statistics
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Statistics
Abstract:Improved estimates of a survey population parameter can be obtained by using information about co-related auxiliary variable. There is a huge literature on such methods for estimating the mean. Here we explore some median-versions of these mean-based methods. Following an introduction in sampling theory and a brief overview on quantile regression, two new quantile based estimators are introduced and some of their properties are examined. A proof for consistency of the marginal quantile estimator and for Bahadur-Expansion validity of the conditional quantile estimator is included. Then, a look by means of the conditional double exponential likelihood model reveals the quantile base estimator connections to some of the already classical estimators. Some simulation results exploring the performance of the new estimators conclude the presentation.
Issue Date:2004
Type:Text
Language:English
Description:78 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004.
URI:http://hdl.handle.net/2142/87397
Other Identifier(s):(MiAaPQ)AAI3153300
Date Available in IDEALS:2015-09-28
Date Deposited:2004


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