Flexible and Robust Nonlinear Statistical Modeling Based on High-Dimensional Complex Heterogeneous Data Analysis

点击翻译按钮获取中文摘要

We developed robust statistical methods for extracting valuable information from various types of high-dimensional heterogeneous data. As the results, when a subset of feature variables is defined a priori, we develop a series of statistical methods that can evaluate whether the subset has a unique distribution by comparing with background variables in the given large dataset. We also proposed a robust estimation method for large gene network from microarray gene expression data and other types of data like transcription binding sites and applied it to the analysis of cancer heterogeneity.