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Hidden Markov chains in generalized linear models
Author(s) -
Turner T. Rolf,
Cameron Murray A.,
Thomson Peter J.
Publication year - 1998
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315677
Subject(s) - markov chain , hidden markov model , class (philosophy) , variable order markov model , covariance , fisher information , matrix (chemical analysis) , covariance matrix , computer science , mathematics , algorithm , markov model , missing data , generalized linear model , linear model , pattern recognition (psychology) , artificial intelligence , statistics , materials science , composite material
We show how the concept of hidden Markov model may be accommodated in a setting involving multiple sequences of observations. The resulting class of models allows for both interrelationships between different sequences and serial dependence within sequences. Missing values in the observation sequences may be handled in a straightforward manner. We also examine a group of methods, based upon the observed Fisher Information matrix, for estimating the covariance matrix of the parameter estimates. We illustrate the methods with both real and simulated data sets.