Work

Complex sampling design based inference on familial models for count data

Year: 2007

Type: dissertation

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.

Source: Memorial University Research Repository (Memorial University)

Author: Lauren Irene Granter

Language: English

Topic: Survey Sampling and Estimation Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2178529079

Cites: 6

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Related to: 10