Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43418
Title: Robust estimation for longitudinal data with informative observation times
Authors: Liu, Ky
Zhao, X 
Keywords: 62G08
Estimating equation
Informative observation process
Longitudinal data
Model checking
MSC 2010: Primary 62J05
Robust estimation
Secondary 62E20
Issue Date: 2015
Publisher: John Wiley & Sons
Source: Canadian journal of statistics, 2015, v. 43, no. 4, p. 519-533 How to cite?
Journal: Canadian journal of statistics 
Abstract: In this paper we focus on regression analysis of irregularly observed longitudinal data that often occur in medical follow-up studies and observational investigations. The analysis of these data involves two processes. One is the underlying longitudinal response process of interest and the other is the observation process that controls observation times. Most of the existing methods, however, rely on some restrictive models or assumptions such as the Poisson assumption. For this we propose a class of more flexible joint models and a robust estimation approach for regression analysis of longitudinal data with related observation times. The asymptotic properties of the proposed estimators are established and a model checking procedure is also presented. The numerical studies indicate that the proposed methods work well for practical situations.
URI: http://hdl.handle.net/10397/43418
ISSN: 0319-5724 (print)
1708-945X (online)
DOI: 10.1002/cjs.11269
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