Date: 28 March 2018 (Wednesday)
Time: 3:30-4:30pm
Venue: T7-502
Speaker: Dr. Xiaoling PENG
Language: English
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Title:Variable Selection Methods and Their Applications

Abstract: High dimensional data analysis has become increasingly frequent and important in diverse fields of sciences, engineering, and humanities, ranging from genomics and health sciences to economics, finance and machine learning. Variable selection plays a pivotal role in contemporary statistical learning and scientific discoveries. The traditional idea of best subset selection methods, which can be regarded as a specific form of penalized likelihood, is computationally too expensive for many modern statistical applications. Other forms of penalized likelihood methods have been successfully developed over the last decade to cope with high dimensionality. They have been widely applied for simultaneously selecting important variables and estimating their effects in high dimensional statistical inference. In this lecture, we present a brief introduction of the recent developments of theory, methods, and implementations for high dimensional variable selection. We also review some recent advances in L0 penalized variable selection approaches together with their pros and cons.