title: Exact likelihood evaluation in a Markov mixture model for time series of seizure counts.
abstract: This paper provides an alternative to Albert's (1991), Biometrics 47, 1371-1381) approximation to the E-step when using the EM algorithm for parameter estimation in Markov mixture models. Use of a recursive algorithm of Baum et al. (1970, Annals of Mathematical Statistics 41, 164-171) results in exact evaluation of the likelihood, optimal parameter estimates, and very efficient computation. Applications to time series of seizure counts and fetal movements clearly show the advantages of this exact approach.
authors.affiliation: Department of Statistics, University of British Columbia, Vancouver, Canada.
authors.forename: N D
authors.initials: ND
authors.lastname: Le
authors.forename: B G
authors.initials: BG
authors.lastname: Leroux
authors.forename: M L
authors.initials: ML
authors.lastname: Puterman
mesh_terms: D000465:Algorithms; D001699:Biometry; D005333:Fetus / Q000502:physiology; D006801:Humans; D007223:Infant; D016013:Likelihood Functions; D008390:Markov Chains; D015233:Models, Statistical; D009068:Movement; D012640:Seizures / Q000453:epidemiology*