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Combining Test Statistics and Information Criteria for High Dimensional Data Integration

Combining Test Statistics and Information Criteria for High Dimensional Data Integration

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Title: Combining Test Statistics and Information Criteria for High Dimensional Data Integration
Author: Xu, Yawen
Abstract: This research is focused on high dimensional data integration by
combing test statistics or information criteria. Our research contains four projects. Firstly, an integration method is
developed to perform hypothesis testing and biomarkers selection
based on multi-platform data sets observed from normal and diseased
populations. Secondly, non-parametric method is developed to cluster
continuous data mixed with categorical data, where modified
Chi-squared tests are used to detect of cluster patterns on the
product space. Thirdly, weighted integrative AICs criterion is
developed to be used for model selection across multiple data sets. Finally,
Linhart's and Shimodaria's test statistics are extended onto
composite likelihood function to perform model comparison test for
correlated data.
Subject: Statistics
Keywords: Statistics
Data integration
Clustering
Model selection
Model comparison
Type: Electronic Thesis or Dissertation
Rights: Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
URI: http://hdl.handle.net/10315/30093
Supervisor: Gao, Xin ; Wang, Xiaogang
Degree: PhD - Doctor of Philosophy
Program: Mathematics & Statistics
Exam date: 2015-04-17
Publish on: 2015-08-28

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