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Repeated Measures Mixture Modeling with Applications to Neuroscience

Sun, Zhuoxin (2005) Repeated Measures Mixture Modeling with Applications to Neuroscience. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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In some neurological postmortem brain tissue studies, repeated measures are observed. These observations are taken on the same experimental subject and are therefore correlated within the subject. Furthermore, each observation can be viewed as coming from one of a pre-specified number of populations where each population corresponds to a possible type of neurons. In this dissertation, we propose several mixture models with two components to model such repeated data. In the first model, we include subject-specific random effects in the component distributions to account for the within-subject correlation present in the data. The mixture components are generalized linear models with random effects, while the mixing proportions are governed by a logistic regression. In the second proposed model, the mixture components are generalized linear models, while the component-indicator variables are modeled by a multivariate Bernoulli distribution that depends on covariates. The within-subject observations are taken to be correlated through the latent component indicator random variables. As a special case of the second model, we focus on multivariate Bernoulli mixtures of normals, where the component-indicator variables are modeled by logistic regressions with random effects, and the mixture components are linear regressions. The third proposed model combines the first and second models, so that the within-subject correlation is built into the model not only through the component distributions, but also through the latent component indicator variables. The focus again is on a special case of the third model, where the mixture components are linear regressions with random effects while the mixing proportions are logistic regressions with another group of random effects. For each model, model fitting procedures, based on MCMC methods for sampling from the posterior distribution of the parameters, are developed. The second and third model are used to compare schizophrenic and control subjects with regard to the somal volumes of deep layer 3 pyramidal cells in the auditory association cortex. As a preliminary analysis, we start by employing classic mixture models and mixtures-of-experts to analyze such data neglecting the within-subject correlation. We also provide a discussion of the statistical and computational issues concerning estimation of classic Poisson mixtures.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Sun, Zhuoxinzhsst3@pitt.eduZHSST3
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee CoChairSampson, Allan Rasampson@stat.pitt.eduASAMPSON
Committee CoChairRosen,
Committee MemberBlock, Henry Whwb@stat.pitt.eduHWB
Committee MemberSweet, Robert AsweetRA@upmc.eduSWEET
Committee MemberKass, Robert
Date: 6 June 2005
Date Type: Completion
Defense Date: 31 January 2005
Approval Date: 6 June 2005
Submission Date: 8 April 2005
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Statistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: MCMC; Mixture models; Mixtures-of-experts; Multivariate Bernoulli distribution; Repeated measures
Other ID:, etd-04082005-204826
Date Deposited: 10 Nov 2011 19:35
Last Modified: 19 Dec 2016 14:35


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