A two-state Markov mixture model for a time series of epileptic seizure counts.
This paper discusses a model for a time series of epileptic seizure counts in which the mean of a Poisson distribution changes according to an underlying two-state Markov chain. The EM algorithm (Dempster, Laird, and Rubin, 1977, Journal of the Royal Statistical Society, Series B 39, 1-38) is used to compute maximum likelihood estimators for the parameters of this two-state mixture model and extensions are made allowing for nonstationarity. The model is illustrated using daily seizure counts for patients with intractable epilepsy and results are compared with a simple Poisson distribution and Poisson regressions. Some simulation results are also presented to demonstrate the feasibility of this model.
Albert
P S
PS
Biometry and Field Studies Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health.
D001699:Biometry; D004827:Epilepsy / Q000503:physiopathology*; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D016012:Poisson Distribution; D012044:Regression Analysis; D013997:Time Factors