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Goodness-of-Fit Tests for Generalize...
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ProQuest Information and Learning Co.
Goodness-of-Fit Tests for Generalized Linear Mixed Models.
Record Type:
Language materials, manuscript : Monograph/item
Title/Author:
Goodness-of-Fit Tests for Generalized Linear Mixed Models./
Author:
Dao, Cecilia Uyen.
Description:
1 online resource (51 pages)
Notes:
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:
Dissertation Abstracts International79-04B(E).
Subject:
Statistics. -
Online resource:
click for full text (PQDT)
ISBN:
9780355451870
Goodness-of-Fit Tests for Generalized Linear Mixed Models.
Dao, Cecilia Uyen.
Goodness-of-Fit Tests for Generalized Linear Mixed Models.
- 1 online resource (51 pages)
Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Thesis (Ph.D.)
Includes bibliographical references
We propose a modified version of Pearson's chi2 test for goodness-of-fit that is applicable to generalized linear mixed models (GLMMs) diagnostics. The proposed test is based on cell frequencies, which is natural for many cases of GLMM. The procedure is simple and does not involve generalized inverse of a matrix, as was used in a previous study. Furthermore, the unknown parameters are estimated by solving a system of optimal estimating equations, which is computationally more efficient than the maximum likelihood estimators that were used in the previous study. Finally, the asymptotic null distribution of the proposed test is chi2M--r--1 , where M is the number of cells and r is the number of unknown parameters that are estimated. Asymptotic theory is established for GLMMs with clustered random effects as well as GLMMs with crossed random effects. Simulation studies are carried out to demonstrate the asymptotic theory as well as finite-sample performance of the proposed test, including comparison with the previous method and other standard goodness-of-fit tests. An example of real-data application is considered.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018
Mode of access: World Wide Web
ISBN: 9780355451870Subjects--Topical Terms:
556824
Statistics.
Index Terms--Genre/Form:
554714
Electronic books.
Goodness-of-Fit Tests for Generalized Linear Mixed Models.
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Adviser: Jiming Jiang.
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2017.
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Includes bibliographical references
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We propose a modified version of Pearson's chi2 test for goodness-of-fit that is applicable to generalized linear mixed models (GLMMs) diagnostics. The proposed test is based on cell frequencies, which is natural for many cases of GLMM. The procedure is simple and does not involve generalized inverse of a matrix, as was used in a previous study. Furthermore, the unknown parameters are estimated by solving a system of optimal estimating equations, which is computationally more efficient than the maximum likelihood estimators that were used in the previous study. Finally, the asymptotic null distribution of the proposed test is chi2M--r--1 , where M is the number of cells and r is the number of unknown parameters that are estimated. Asymptotic theory is established for GLMMs with clustered random effects as well as GLMMs with crossed random effects. Simulation studies are carried out to demonstrate the asymptotic theory as well as finite-sample performance of the proposed test, including comparison with the previous method and other standard goodness-of-fit tests. An example of real-data application is considered.
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Ann Arbor, Mich. :
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ProQuest,
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2018
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Mode of access: World Wide Web
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ProQuest Information and Learning Co.
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click for full text (PQDT)
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