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Correlated GMM Logistic Regression Models with Time-Dependent Covariates and Valid Estimating Equations


Abstract When analyzing longitudinal data it is essential to account both for the correlation inherent from the repeated measures of the responses as well as the correlation realized on account of the feedback created between the responses at a particular time and the predictors at other times. A generalized method of moments (GMM) for estimating the coefficients in longitudinal data is presented. The appropriate and valid estimating equations associated with the time-dependent covariates are identified, thus providing substantial gains in efficiency over generalized estimating equations (GEE) with the independent working correlation. Identifying the estimating equations for computation is of utmost importance. This paper provides a technique for id... (more)
Created Date 2012
Contributor Yin, Jianqiong (Author) / Wilson, Jeffrey Wilson (Advisor) / Reiser, Mark (Committee member) / Kao, Ming-Hung (Committee member) / Arizona State University (Publisher)
Subject Statistics / estimating equations / generalized method of moments / longitudinal data / time dependency
Type Masters Thesis
Extent 48 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note M.S. Statistics 2012
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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