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Structured Modeling for Post-Mortem Brain Tissue Data.

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Τίτλος: Structured Modeling for Post-Mortem Brain Tissue Data.
Συγγραφείς: Wu, Qiang1 (AUTHOR) wuq@ecu.edu, Sampson, AllanR.2 (AUTHOR)
Πηγή: Communications in Statistics: Theory & Methods. 2012, Vol. 41 Issue 7, p1194-1213. 20p. 3 Charts, 4 Graphs.
Θεματικοί όροι: *Mathematical models, *Data analysis, *Multivariate analysis, Nerve tissue, Brain, Autopsy, Missing data (Statistics), Neurosciences
Περίληψη: Structured means have been used in studying possible covariate effects on responses, whereas patterned covariances deal with random effects, missing data, and differing study designs. In this article, we develop new multivariate models with patterned means and covariance matrices to deal with special structures of the post-mortem brain tissue data collected in the Conte Center for the Neuroscience of Mental Disorders at the University of Pittsburgh. We obtain maximum likelihood estimates via the method of scoring for these new structured models. One-iteration estimators from a consistent starting point are used to derive the asymptotic distributions. The model fitting algorithms, as well as the asymptotic distributions, are examined using simulated data, and are applied to data from post-mortem tissue studies in schizophrenia. [ABSTRACT FROM AUTHOR]
Copyright of Communications in Statistics: Theory & Methods is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Structured means have been used in studying possible covariate effects on responses, whereas patterned covariances deal with random effects, missing data, and differing study designs. In this article, we develop new multivariate models with patterned means and covariance matrices to deal with special structures of the post-mortem brain tissue data collected in the Conte Center for the Neuroscience of Mental Disorders at the University of Pittsburgh. We obtain maximum likelihood estimates via the method of scoring for these new structured models. One-iteration estimators from a consistent starting point are used to derive the asymptotic distributions. The model fitting algorithms, as well as the asymptotic distributions, are examined using simulated data, and are applied to data from post-mortem tissue studies in schizophrenia. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Communications in Statistics: Theory & Methods is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/03610926.2010.539745
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 20
        StartPage: 1194
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      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Multivariate analysis
        Type: general
      – SubjectFull: Nerve tissue
        Type: general
      – SubjectFull: Brain
        Type: general
      – SubjectFull: Autopsy
        Type: general
      – SubjectFull: Missing data (Statistics)
        Type: general
      – SubjectFull: Neurosciences
        Type: general
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      – TitleFull: Structured Modeling for Post-Mortem Brain Tissue Data.
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              Text: 2012
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              Y: 2012
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