Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6870163 | Computational Statistics & Data Analysis | 2014 | 15 Pages |
Abstract
For the analysis of longitudinal data with nonignorable and nonmonotone missing responses, a full likelihood method often requires intensive computation, especially when there are many follow-up times. The authors propose and explore a Monte Carlo method, based on importance sampling, for approximating the maximum likelihood estimators. The finite-sample properties of the proposed estimators are studied using simulations. An application of the proposed method is also provided using longitudinal data on peptide intensities obtained from a proteomics experiment of trauma patients.
Keywords
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Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Sanjoy K. Sinha, Amit Kaushal, Wenzhong Xiao,