Combining Test Statistics and Information Criteria for High Dimensional Data Integration

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.