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Goodness-of-Fit Tests for Generalize...

ProQuest Information and Learning Co.

Goodness-of-Fit Tests for Generalized Linear Mixed Models.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Goodness-of-Fit Tests for Generalized Linear Mixed Models./
作者:	Dao, Cecilia Uyen.
面頁冊數:	1 online resource (51 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
標題:	Statistics. -
電子資源:	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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100 1 $a Dao, Cecilia Uyen. $3 1183017
245 1 0 $a Goodness-of-Fit Tests for Generalized Linear Mixed Models.
264 0 $c 2017
300 $a 1 online resource (51 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Adviser: Jiming Jiang.
502 $a Thesis (Ph.D.) $c University of California, Davis $d 2017.
504 $a Includes bibliographical references
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Statistics. $3 556824
655 7 $a Electronic books. $2 local $3 554714
690 $a 0463
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Davis. $b Statistics. $3 1182924
773 0 $t Dissertation Abstracts International $g 79-04B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10624275 $z click for full text (PQDT)

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