Complex sampling design based inference on familial models for count data

Granter, Lauren Irene (2007) Complex sampling design based inference on familial models for count data. Masters thesis, Memorial University of Newfoundland.

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Abstract

Consistent and efficient estimation of the parameters of generalized linear mixed models (GLMMs) has proven to be difficult in the infinite population setup. This estimation issue becomes more complex in the infinite population setup where the estimation is done based on a sample of a small number of clusters chosen from a finite population with a large number of unequally sized clusters. This practicum examines the role of the sampling designs on the estimation of the parameters of the GLMM based super-population for clustered count data.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/9106
Item ID: 9106
Additional Information: Includes bibliographical references (leaves 52-53)
Department(s): Science, Faculty of > Mathematics and Statistics
Date: 2007
Date Type: Submission
Library of Congress Subject Heading: Cluster analysis; Linear models (Statistics); Parameter estimation; Sampling (Statistics)

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