From stopping times to “spotting” times : a new framework for multiple testing

Neyman Seminar
Jan 17, 2018 4:00pm to 5:00pm
1011 Evans Hall
Happening As Scheduled
Modern data science is often exploratory in nature, with hundreds or thousands of hypotheses being regularly tested on scientific datasets. The false discovery rate (FDR) has emerged as a dominant error metric in multiple hypothesis testing over the last two decades. I will argue that both (a) the FDR error metric, as well as (b) the current framework of multiple testing, where the scientist...
Aaditya Ramdas, UC Berkeley