A hidden Markov model approach to analyze longitudinal ternary outcomes when some observed states are possibly misclassified-Reference-Cited by-同舟云学术

A hidden Markov model approach to analyze longitudinal ternary outcomes when some observed states are possibly misclassified

Published:2016-01-18 Issue:9 Volume:35 Page:1549-1557
ISSN:0277-6715
Container-title:Statistics in Medicine
language:en
Short-container-title:Statist. Med.

Author:

Benoit Julia S.¹²,Chan Wenyaw²,Luo Sheng²^ORCID,Yeh Hung-Wen³,Doody Rachelle⁴

Affiliation:

1. Texas Institute for Measurement, Evaluation, and Statistics and Department of Basic Vision Sciences, College of Optometry; The University of Houston; Houston TX 77204 U.S.A.

2. Department of Biostatistics; The University of Texas Health Science Center at Houston; Houston TX 77030 U.S.A.

3. Department of Biostatistics; The University of Kansas Medical Center; Kansas City KS 66160 U.S.A.

4. Alzheimer's Disease and Memory Disorders Center, Department of Neurology; Baylor College of Medicine; Houston TX 77030 U.S.A.

Funder

National Institutes of Health

Publisher

Wiley

Subject

Statistics and Probability,Epidemiology

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4. Monitoring epidemiologic surveillance data using hidden Markov models;Le Strat;Statistics in Medicine,1999

5. The estimation of gross flows in the presence of measurement error using auxiliary variables;Pfeffemann;Journal of the Royal Statistical Society. Series A (Statistics in Society),1998

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