Document Detail


A two-state Markov mixture model for a time series of epileptic seizure counts.
MedLine Citation:
PMID:  1786324     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
P S Albert
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Biometrics     Volume:  47     ISSN:  0006-341X     ISO Abbreviation:  Biometrics     Publication Date:  1991 Dec 
Date Detail:
Created Date:  1992-03-20     Completed Date:  1992-03-20     Revised Date:  2004-11-17    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  1371-81     Citation Subset:  IM    
Affiliation:
Biometry and Field Studies Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health.
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MeSH Terms
Descriptor/Qualifier:
Biometry
Epilepsy / physiopathology*
Humans
Likelihood Functions
Markov Chains
Models, Biological*
Models, Statistical
Poisson Distribution
Regression Analysis
Time Factors

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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