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Estimation in Markov Models from Aggregate Data
J. D. Kalbfleisch, J. F. Lawless and W. M. Vollmer
Vol. 39, No. 4 (Dec., 1983), pp. 907-919
Published by: International Biometric Society
Stable URL: http://www.jstor.org/stable/2531326
Page Count: 13
You can always find the topics here!Topics: Least squares, Markov models, Markov processes, Eigenvalues, Biometrics, Statistical estimation, Instars, Maximum likelihood estimation, Matrices, Estimators
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In this paper, situations in which individuals move through a finite set of states according to a continuous-time Markov process are considered. Only aggregate data are available: these consist of the number of individuals in each state at specified observation times. We develop conditional least squares and approximate maximum-likelihood-estimation procedures for time-homogeneous models, and extend the methods so that they can handle immigration of individuals into the system during observation. Asymptotic covariance estimates are presented, and some problems for future study are noted.
Biometrics © 1983 International Biometric Society