Inferences in non-stationary longitudinal binary models

Wooden, Tracey Carolanne (2013) Inferences in non-stationary longitudinal binary models. Masters thesis, Memorial University of Newfoundland.

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Longitudinal binary data has been analyzed over the last three decades either by using odds ratio or 'working' correlations as a measure of association between the repeated binary responses. Recently, this type of data has been analyzed by modeling the correlations parametrically and estimating the parameters by a generalized quasi-likelihood (GQL) approach. In this thesis, we consider a specific correlation model, namely, the binary autoregressive order 1 (AR(1)) model to generate the data, and study the relative performance of the odds ratio and equi-correlations based estimation approaches with the GQL approach. This comparison is mainly done by simulations under both stationary and non-stationary AR(1) correlation models. A real life data set containing repeated asthma status of a group of children is also analyzed.

Item Type: Thesis (Masters)
Item ID: 9963
Additional Information: Includes bibliographical references (leaves 92-94).
Department(s): Science, Faculty of > Mathematics and Statistics
Date: 2013
Date Type: Submission
Library of Congress Subject Heading: Binary system (Mathematics); Longitudinal method; Correlation (Statistics); Estimation theory--Computer programs.

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