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Thesis.pdf (305.16 KB)
ETD Abstract Container
Abstract Header
Statistical Methods for Biological and Relational Data
Author Info
Anderson, Sarah G
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=osu1365441350
Abstract Details
Year and Degree
2013, Master of Science, Ohio State University, Biostatistics.
Abstract
Methods for biological and relational data have pose challenges for statistical modeling. For biological data, gene expression data have high-dimensionality, and T-cell receptor (TCR) data under-sample receptor populations. For relational data, there are dependencies among the observations. This thesis outlines statistical methods for biological and relational data. The methods include classification, multiple testing and social networking. The models for classification are applied to gene expression data. The first method looks at variable selection to show the usefulness of sequential classification and regression trees to more advance methods. The second method uses Monte Carlo methods to calculate a rank for variable selection using supervised classification. Multiple testing methods are applied to gene expression and TCR data. The first method for gene expression looks at strong control of the familywise error rate without the assumption of the subset pivotality property, which is generally not met for gene expression data. For TCRs, the method extends the Poisson-lognormal model to the bivariate case to simultaneously analyze pairs of repertoires. The relational data uses social networking methods. The first uses exponential random graph models (ERGMs) with the application to political science. Solutions to two limitation of ERGMs, non-binary ties and longitudinal, are presented in examples. The last method proposes a latent position cluster model, an extension of latent class models that models clustering.
Committee
Hong Zhu (Advisor)
Abigail Shoben (Committee Member)
Subject Headings
Biostatistics
Keywords
gene expression
;
T-cell receptors
;
classification
;
multiple testing
;
relational data
;
social networking
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Citations
Anderson, S. G. (2013).
Statistical Methods for Biological and Relational Data
[Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365441350
APA Style (7th edition)
Anderson, Sarah.
Statistical Methods for Biological and Relational Data.
2013. Ohio State University, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=osu1365441350.
MLA Style (8th edition)
Anderson, Sarah. "Statistical Methods for Biological and Relational Data." Master's thesis, Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1365441350
Chicago Manual of Style (17th edition)
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Document number:
osu1365441350
Download Count:
423
Copyright Info
© 2013, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.