Nonparametric test in semiparametric analysis of covariance model for a crossover design with carryover effects / Leonard Allan F. Almero

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Almero, Leonard Allan F

Material type: Text

TextPublication details: Diliman, Quezon City : University of the Philippines Diliman ,2018. Description: ii, 64 leaves ; 29 x 20cmSubject(s):

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Abstract: A semiparametric mixed analysis of covariance model for a crossover design with carryover effects is postulated. A hybrid of restricted maximum likelihood estimation and smoothing splines regression are imbedded into the backfitting algorithm is used to estimate the model. The responses are adjusted for covariate effect through a nonparametric function of the covariates. Simulation study indicates that a bootstrap-based test for variance components is correctly-sized for variance components. The test is powerful in testing for variance component and relatively robust to the magnitude of variance in the alternative hypothesis. Furthermore, the test is advantageous over ordinary analysis of covariance in the presence of misclassification error and in non-normal error on unbalanced data.

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Item type	Current library	Collection	Call number	Status	Date due	Barcode
CHED Funded Research	Commission on Higher Education CHED Funded research		LG 995 2018 C6 A46 (Browse shelf(Opens below))	Storage Area		CHEDFR-000344
CHED Funded Research	Commission on Higher Education Digital Thesis and Dissertation	Digital Thesis and Dissertation	LG 995 2018 C6 A46 (Browse shelf(Opens below))	Available		DCHEDFR-000091

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Thesis (Master of Science in Statistics) -- University of the Philippines Diliman, June 2018.

A semiparametric mixed analysis of covariance model for a crossover design with
carryover effects is postulated. A hybrid of restricted maximum likelihood estimation and
smoothing splines regression are imbedded into the backfitting algorithm is used to estimate
the model. The responses are adjusted for covariate effect through a nonparametric function of
the covariates. Simulation study indicates that a bootstrap-based test for variance components
is correctly-sized for variance components. The test is powerful in testing for variance
component and relatively robust to the magnitude of variance in the alternative hypothesis.
Furthermore, the test is advantageous over ordinary analysis of covariance in the presence of
misclassification error and in non-normal error on unbalanced data.

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