Kerby Shedden: Dimension Reduction Regression скачать в хорошем качестве

Kerby Shedden: Dimension Reduction Regression

Abstract: Over the past 30 years, a set of tools for regression analysis known as "Dimension Reduction" (DR) regression has been developed by researchers in statistics. However it is fair to say that DR regression is seldom used in professional statistical work. In this talk I will review some of the more useful and easily-grasped DR methods, and present several case studies from human health and biology to demonstrate their strengths and limitations. In recent years, tools for statistical inference have been gradually developed that fill some of the gaps that previously may have limited the use of DR regression methods in data analysis for science -- in my presentation I will place special emphasis on these advances. Bio: Kerby Shedden received his PhD in Statistics from UCLA in 1999 and joined the University of Michigan the same year. His research interests include genomics, genetics, and other areas of life science where large and complex data arise. He also is interested in computational statistics and statistical software development. He participates in many collaborative research efforts including biomarker screening for cancer and kidney disease outcomes, cell-based screening for understanding the behavior of chemical probes in cells, and genetic association analysis for longitudinal traits.