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Goodness-of-Fit Tests in Measurement...

Jia, Weijia.

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Goodness-of-Fit Tests in Measurement Error Models With Replications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Goodness-of-Fit Tests in Measurement Error Models With Replications./
Author:	Jia, Weijia.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	117 p.
Notes:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Contained By:	Dissertation Abstracts International79-11B(E).
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10785743
ISBN:	9780438123229

Goodness-of-Fit Tests in Measurement Error Models With Replications.
Jia, Weijia.

Goodness-of-Fit Tests in Measurement Error Models With Replications. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 117 p.

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.

Thesis (Ph.D.)--Kansas State University, 2018.

In this dissertation, goodness-of-fit tests are proposed for checking the adequacy of parametric distributional forms of the regression error density functions and the error-prone predictor density function in measurement error models, when replications of the surrogates of the latent variables are available.

ISBN: 9780438123229Subjects--Topical Terms:

517247
Statistics.

Goodness-of-Fit Tests in Measurement Error Models With Replications.
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245 1 0 $a Goodness-of-Fit Tests in Measurement Error Models With Replications.
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300 $a 117 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
500 $a Adviser: Weixing Song.
502 $a Thesis (Ph.D.)--Kansas State University, 2018.
520 $a In this dissertation, goodness-of-fit tests are proposed for checking the adequacy of parametric distributional forms of the regression error density functions and the error-prone predictor density function in measurement error models, when replications of the surrogates of the latent variables are available.
520 $a In the first project, we propose goodness-of-fit tests on the density function of the regression error in the errors-in-variables model. Instead of assuming that the distribution of the measurement error is known as is done in most relevant literature, we assume that replications of the surrogates of the latent variables are available. The test statistic is based upon a weighted integrated squared distance between a nonparametric estimate and a semi-parametric estimate of the density functions of certain residuals. Under the null hypothesis, the test statistic is shown to be asymptotically normal. Consistency and local power results of the proposed test under fixed alternatives and local alternatives are also established. Finite sample performance of the proposed test is evaluated via simulation studies. A real data example is also included to demonstrate the application of the proposed test.
520 $a In the second project, we propose a class of goodness-of-fit tests for checking the parametric distributional forms of the error-prone random variables in the classic additive measurement error models. We also assume that replications of the surrogates of the error-prone variables are available. The test statistic is based upon a weighted integrated squared distance between a nonparametric estimator and a semi-parametric estimator of the density functions of the averaged surrogate data. Under the null hypothesis, the minimum distance estimator of the distribution parameters and the test statistics are shown to be asymptotically normal. Consistency and local power of the proposed tests under fixed alternatives and local alternatives are also established. Finite sample performance of the proposed tests is evaluated via simulation studies.
590 $a School code: 0100.
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710 2 $a Kansas State University. $b Department of Statistics. $3 1272944
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