A novel mixture model is presented for repeated measurements in which correlation among repeated observations on the same subject is induced via correlated unobservable component indicators. The mixture components in our model are linear regressions, and the mixing proportions are logits with random effects. Inference is facilitated by sampling from the posterior distribution of the parameters via Markov chain Monte Carlo methods. The model is applied to a neuronal postmortem brain tissue study to examine the differences in neuron volumes between schizophrenic and control subjects.
Multivariate bernoulli mixture models with application to postmortem tissue studies in schizophrenia. Publishing Authors By Initials
Multivariate bernoulli mixture models with application to postmortem tissue studies in schizophrenia. Journal Published:
PUBLICATION TYPE: Research Support, U.S. Gov't,
Journal: Biometrics
VOLUME: 63
Page Numbers: 901-9
Journal Abbreviation: Biometrics
ISSN: 0006-341X
DAY: 3
MONTH: Sep
YEAR: 2007
Multivariate bernoulli mixture models with application to postmortem tissue studies in schizophrenia. Information
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LANGUAGE: eng
NlmUniqueID: 370625
Multivariate bernoulli mixture models with application to postmortem tissue studies in schizophrenia. Keywords Mesh Terms:
KEYWORDS: Statistical Distributions
MESH TERMS: pathology
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Grant and Affiliation Information for Multivariate bernoulli mixture models with application to postmortem tissue studies in schizophrenia.
AFFILIATION: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, Massachusetts 02115, USA. zhuoxin@jimmy.harvard.edu
Country: United States
AGENCY: United States NIMH
GRANT: P50 MH045156
ACRONYM: MH
MEDLINETA: Biometrics
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