715 Broadway, 12th Floor, Room 1221
Please join the Department of Biostatistics for this exciting event featuring Professor Yoav Benjamini.
The replicability problems across varied scientific disciplines has attracted increasing attention in the last two decades. Unadjusted inference on the few promising ones, selected as such, is a major source of the problems. There are a few strategies for addressing such selective inference, which will be reviewed, and many related methodologies which will not. Unfortunately, the problem is ignored in many important and highly visible areas of science. After presenting this background, the talk will focus on two specific issues: a less trodden strategy, that of offering simultaneous inference on the selected, and a methodology, that of addressing selective inference in a hierarchical system of inferences. I shall describe some recent results on these two, as well as open questions. Returning to science at large, inference on hierarchical systems will be used to address the problem of selective inference when a database is interrogated by different investigators.
Yoav Benjamini is the Nathan and Lily Silver Professor of Applied Statistics at the Department of statistics and Operations Research at Tel Aviv University. He holds BSc. In Physics and BSc and MSc. In mathematics from the Hebrew University (1976), and PhD in Statistics from Princeton University (1981). He is a member of the Sagol School of Neuroscience and the Edmond Safra Bioinformatics Center. He was a visiting professor at U of Pennsylvania, UC Berkeley, Stanford and Columbia Universities. Prof. Benjamini is a co-developer of the widely used and cited False Discovery Rate concept and methodology. His research topics are selective and simultaneous inference, replicability and reproducibility in science, and data mining, with applications in Biostatistics, Bioinformatics, Animal Behavior, Brain Imaging and Health Informatics. He is a member of the Israel Academy of Sciences and Humanities and a recipient of the Israel Prize in Statistics and Economics and the Karl Pearson Prize of the International Statistical Institute.
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