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Applications of nonparametric regres...

Li, Xiaoxi.

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Applications of nonparametric regression in survey statistics.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Applications of nonparametric regression in survey statistics./
Author:	Li, Xiaoxi.
Description:	135 p.
Notes:	Adviser: Jean D. Opsomer.
Contained By:	Dissertation Abstracts International67-11B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3243537
ISBN:	9780542993824

Applications of nonparametric regression in survey statistics.
Li, Xiaoxi.

Applications of nonparametric regression in survey statistics. - 135 p.

Adviser: Jean D. Opsomer.

Thesis (Ph.D.)--Iowa State University, 2006.

Systematic sampling is a frequently used sampling method in natural resource surveys, because of its ease of implementation and its design efficiency. An important drawback of systematic sampling, however, is that no direct estimator of the design variance is available. We propose an estimator of the model-based expectation of the design variance, under a nonparametric model for the population. The nonparametric model is sufficiently flexible that it can be expected to hold at least approximately for many practical situations. We prove that the nonparametric variance estimator is both a consistent estimator for the model-based expectation of the design variance and a consistent predictor for the design variance in the model-based context. This variance estimator's properties are further explored through a simulation study. An application in Forest Inventory and Analysis (FIA) is discussed in the second chapter. We compare the nonparametric variance estimator with the variance estimators for random stratified sampling and simple random sampling. The nonparametric variance estimator performs very well and it also has the advantage of allowing more complex models. A discussion about selecting proper auxiliary variables is also carried out for this application. In the last chapter, we study model averaging in survey estimation. Model averaging is a widely used method as it accounts for uncertainties in model selection. However, its applications in survey estimation are yet to be explored. We propose a model-averaging (MA) regression estimator for the population total. The goal is to provide a method that will work well for a wide range of response variables and situations. Different ways to obtain this estimator are explored through large-scale simulation studies.

ISBN: 9780542993824Subjects--Topical Terms:

517247
Statistics.

Applications of nonparametric regression in survey statistics.
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005 20110831
008 110831s2006 ||||||||||||||||| ||eng d
020 $a 9780542993824
035 $a (UMI)AAI3243537
035 $a AAI3243537
040 $a UMI $c UMI
100 1 $a Li, Xiaoxi. $3 1286980
245 1 0 $a Applications of nonparametric regression in survey statistics.
300 $a 135 p.
500 $a Adviser: Jean D. Opsomer.
500 $a Source: Dissertation Abstracts International, Volume: 67-11, Section: B, page: 6485.
502 $a Thesis (Ph.D.)--Iowa State University, 2006.
520 $a Systematic sampling is a frequently used sampling method in natural resource surveys, because of its ease of implementation and its design efficiency. An important drawback of systematic sampling, however, is that no direct estimator of the design variance is available. We propose an estimator of the model-based expectation of the design variance, under a nonparametric model for the population. The nonparametric model is sufficiently flexible that it can be expected to hold at least approximately for many practical situations. We prove that the nonparametric variance estimator is both a consistent estimator for the model-based expectation of the design variance and a consistent predictor for the design variance in the model-based context. This variance estimator's properties are further explored through a simulation study. An application in Forest Inventory and Analysis (FIA) is discussed in the second chapter. We compare the nonparametric variance estimator with the variance estimators for random stratified sampling and simple random sampling. The nonparametric variance estimator performs very well and it also has the advantage of allowing more complex models. A discussion about selecting proper auxiliary variables is also carried out for this application. In the last chapter, we study model averaging in survey estimation. Model averaging is a widely used method as it accounts for uncertainties in model selection. However, its applications in survey estimation are yet to be explored. We propose a model-averaging (MA) regression estimator for the population total. The goal is to provide a method that will work well for a wide range of response variables and situations. Different ways to obtain this estimator are explored through large-scale simulation studies.
590 $a School code: 0097.
650 4 $a Statistics. $3 517247
690 $a 0463
710 2 $a Iowa State University. $3 1017855
773 0 $t Dissertation Abstracts International $g 67-11B.
790 $a 0097
790 1 0 $a Opsomer, Jean D., $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3243537