Generalized quasilikelihood method for misclassified longitudinal binary data

Tao, Yi (2010) Generalized quasilikelihood method for misclassified longitudinal binary data. Masters thesis, Memorial University of Newfoundland.

[img] [English] PDF - Accepted Version
Available under License - The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission.

Download (11MB)


In this practicum we develop the generalized quasi-likelihood approach to analyzing longitudinal binary data with misclassification in response. We utilize the method of Monahan and Stefanski (1992) to approximate the expectation of an unknown function involved in the calculation of the means and covariances, which are further used to develop GQL estimating functions. The results of an intensive simulation study show that the proposed method works very well in all the preselected settings. The efficiency gain as compared to the naive method is remarkable. The method is robust in the sense that the performance varies just slightly when model parameters change in the simulation. -- Keywords: Logit link; longitudinal binary response; GQL; Misclassification.

Item Type: Thesis (Masters)
Item ID: 9477
Additional Information: Bibliography: leaves 68-70.
Department(s): Science, Faculty of > Mathematics and Statistics
Date: 2010
Date Type: Submission
Library of Congress Subject Heading: Biometry; Linear models (Statistics); Logistic distribution; Regression analysis

Actions (login required)

View Item View Item


Downloads per month over the past year

View more statistics