Research in Bayesian Analysis: Large-scale Regression and Prediction Models with Applications in Bioinformatics and Applied Time Series

点击翻译按钮获取中文摘要

The PI, collaborators and students develop statistical models and methods for problems involving latent structure in large and complex data sets. Specific research topics include: new models for regression and prediction with latent variables; a range of developments involving latent structure in Bayesian regression models and associated algorithmic implementation using simulation methods; statistical aspects of machine learning algorithms for regression and classification; generalized linear and multivariate latent factor models; models with errors in predictor variables; empirical factor models. Research in these areas is partly motivated by applications in bioinformatics in the area of gene expression profiling, and related research and applications in time series in finance and communications signals processing.This research is concerned with creating and implementing improved methods for the statistical analysis of large and complex data sets arising in fields such as functional genomics, finance and communications engineering. The research goals include creating new mathematical models that can adequately represent the complex structure of increasingly large data sets, together with appropriate computational tools and algorithms for the analysis of such data using these models. The need for substantial advances in this area of statistical research are highlighted by the analysis challenges posed by the increasingly large and complex gene expression data sets that are becoming standard in biomedical research due to rapid advances in genome technology, and major aspects of this research involve applications in functional genomics. Additional applications of models arising from this research lie in analysis of time series data arising in areas including finance and communications.