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Likelihood‐Ratio Tests for Hidden Markov Models
Author(s) -
Giudici Paolo,
Ryden Tobias,
Vandekerkhove Pierre
Publication year - 2000
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2000.00742.x
Subject(s) - hidden markov model , markov model , variable order markov model , markov chain , likelihood ratio test , maximum entropy markov model , computer science , multivariate statistics , set (abstract data type) , markov property , mathematics , hidden semi markov model , statistics , artificial intelligence , machine learning , programming language
Summary. We consider hidden Markov models as a versatile class of models for weakly dependent random phenomena. The topic of the present paper is likelihood‐ratio testing for hidden Markov models, and we show that, under appropriate conditions, the standard asymptotic theory of likelihood‐ratio tests is valid. Such tests are crucial in the specification of multivariate Gaussian hidden Markov models, which we use to illustrate the applicability of our general results. Finally, the methodology is illustrated by means of a real data set.