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Estimation problems from biased samp...

Wang, Bin.

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Estimation problems from biased sampling.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Estimation problems from biased sampling./
Author:	Wang, Bin.
Description:	134 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3358.
Contained By:	Dissertation Abstracts International64-07B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3097367

Estimation problems from biased sampling.
Wang, Bin.

Estimation problems from biased sampling. - 134 p.

Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3358.

Thesis (Ph.D.)--Case Western Reserve University, 2003.

Data from observational studies may come with selection biases. Based on one biased sample, the problem of non-parametrically estimating both the density of a population and the bias function is unidentifiable. However, if there are overlapping samples drawn consecutively, the problem becomes identifiable. Further, an intelligent subject sampled previously may experience a memory effect if sampled again. The first part of this dissertation incorporates the memory effect into a general biased sampling model, with two biased samples from a finite population. Non-parametric estimators of the density, bias, memory functions and the population size are developed. Asymptotic properties of these estimators are studied and confidence bands are provided. Our procedures are compared with those ignoring the memory effect. Their applications are illustrated via simulated data and a real survey data set.Subjects--Topical Terms:

517247
Statistics.

Estimation problems from biased sampling.
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100 1 $a Wang, Bin. $3 1900114
245 1 0 $a Estimation problems from biased sampling.
300 $a 134 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-07, Section: B, page: 3358.
500 $a Adviser: Jiayang Sun.
502 $a Thesis (Ph.D.)--Case Western Reserve University, 2003.
520 $a Data from observational studies may come with selection biases. Based on one biased sample, the problem of non-parametrically estimating both the density of a population and the bias function is unidentifiable. However, if there are overlapping samples drawn consecutively, the problem becomes identifiable. Further, an intelligent subject sampled previously may experience a memory effect if sampled again. The first part of this dissertation incorporates the memory effect into a general biased sampling model, with two biased samples from a finite population. Non-parametric estimators of the density, bias, memory functions and the population size are developed. Asymptotic properties of these estimators are studied and confidence bands are provided. Our procedures are compared with those ignoring the memory effect. Their applications are illustrated via simulated data and a real survey data set.
520 $a In studies involving lifetimes, observed survival times are frequently censored and are possibly selected subject to some selection bias. In the second part of this dissertation, we assume the distribution of the survival times of the population is unknown and the selection function is specified up to an unknown finite dimensional parameter. Under this semi-parametric model with one sample subject to both censoring and selection bias, two estimators are proposed to estimate the density function; a pseudo maximum likelihood estimator is used to estimate the unknown parameter. The identifiability of the problem is discussed. The performance of the new estimators are illustrated via both simulation studies and a real data application.
520 $a Other open problems and possibilities for further research are also discussed.
590 $a School code: 0042.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 0 $a Case Western Reserve University. $3 1017714
773 0 $t Dissertation Abstracts International $g 64-07B.
790 1 0 $a Sun, Jiayang, $e advisor
790 $a 0042
791 $a Ph.D.
792 $a 2003
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3097367