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Estimating hidden morbidity via its effect on mortality and disability
Author(s) -
Woodbury Max A.,
Manton Kenneth G.,
Yashin Anatoli I.
Publication year - 1988
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780070133
Subject(s) - kalman filter , estimation , markov process , maximum likelihood , hidden markov model , markov model , computer science , markov chain , econometrics , mathematics , statistics , artificial intelligence , economics , management
The applicability of the theory of partially observed finite‐state Markov processes to the study of disease. morbidity, and disability is explored. A method is developed for the continuous updating of parameter estimates over time in longitudinal studies analogous to Kalman filtering in continuous valued continuous time stochastic processes. It builds on a model of filtering of incompletely observed finite‐state Markov processes subject to mortality due to Yashin et al. The method of estimation is based on maximum likelihood theory and the incompleteness in the observation of the process is dealt with by applying missing information principles in maximum likelihood estimation.