Estimation in a Markov chain regression model with missing covariates

Dorota M. Dabrowska, Robert M. Elashoff and Donald L. Morton
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Dorota M. Dabrowska: University of California, Department of Biostatistics
Robert M. Elashoff: University of California, Department of Biostatistics
Donald L. Morton: John Wayne Cancer Institute

A chapter in Probability, Statistics and Modelling in Public Health, 2006, pp 90-118 from Springer

Abstract: Summary Markov chain proportional hazard regression model provides a powerful tool for analysis of multiple event times. We discuss estimation in absorbing Markov chains with missing covariates. We consider a MAR model assuming that the missing data mechanism depends on the observed covariates, as well as the number of events observed in a given time period, their types and times of their occurrence. For estimation purposes we use a piecewise constant intensity regression model.

Keywords: Conditional Distribution; Markov Chain Model; Marked Point Process; Miss Data Mechanism; Absorb Markov Chain (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-0-387-26023-5_7

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DOI: 10.1007/0-387-26023-4_7

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