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oai:www.ideals.illinois.edu:2142/87397 2023-07-11 col_2142_5131 col_2142_17362 com_2142_5130 com_2142_17361 com_2142_8903

Stephen Portnoy Georgescu, Constantin 2015-09-28T16:02:42Z 2015-09-28T16:02:42Z 10000-01-01 2004 2004 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. Made available in DSpace on 2015-09-28T16:02:42Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3153300.pdf: 4261762 bytes, checksum: 33637d790457f8e7cec3c7ba94758808 (MD5) Previous issue date: 2004 Embargo set by: Seth Robbins for item 88678 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 78 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004. http://hdl.handle.net/2142/87397 (MiAaPQ)AAI3153300 eng Statistics Finite Population Quantile Estimators text Statistics Statistics University of Illinois at Urbana-Champaign Dissertation Ph.D.