A mixed non-homogeneous hidden Markov model for categorical data, with application to alcohol consumption

Statistics in Medicine

Volumn 31, Issue 9, 2012, Pages 871-886

  Maruotti, Antonello ^a   Rocci, Roberto ^b  

^a ROMA TRE UNIVERSITY   (Italy)

^b UNIVERSITY OF ROME TOR VERGATA   (Italy)

Author keywords

Longitudinal data; Mixed hidden Markov models; Penalized NPML; Random effects models

Indexed keywords

ACCURACY; ALCOHOL ABUSE; ALCOHOL CONSUMPTION; ARTICLE; FINITE ELEMENT ANALYSIS; HIDDEN MARKOV MODEL; HUMAN; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; MAXIMUM LIKELIHOOD METHOD; NONPARAMETRIC TEST; OUTCOME ASSESSMENT; PROBABILITY; PROCESS DEVELOPMENT; PROCESS MODEL; PROCESS OPTIMIZATION; SAMPLE SIZE; STATISTICAL DISTRIBUTION; VALIDATION PROCESS;

ALCOHOL DRINKING; ALGORITHMS; CLINICAL TRIALS AS TOPIC; COMPUTER SIMULATION; HUMANS; LONGITUDINAL STUDIES; MARKOV CHAINS; MODELS, STATISTICAL; NALTREXONE; NARCOTIC ANTAGONISTS;

EID: 84859613477     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.4478     Document Type: Article

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