Daniela Witten - Double dipping: problems and solutions, with application to single-cell RNA-seque..
Автор: Physics Informed Machine Learning
Загружено: 2022-12-15
Просмотров: 896
Описание:
Prof. Daniela Witten of the University of Washington speaking in the UW Data-driven methods in science and engineering seminar on December 2, 2022.
Sign up for notifications of future talks: https://mailman11.u.washington.edu/ma...
Abstract: In contemporary applications, it is common to collect very large data sets with the vaguely-defined goal of hypothesis generation. Once a dataset is used to generate a hypothesis, we might wish to test that hypothesis on the same set of data. However, this type of "double dipping" violates a cardinal rule of statistical hypothesis testing: namely, that we must decide what hypothesis to test before looking at the data. When this rule is violated, then standard statistical hypothesis tests (such as t-tests and z-tests) fail to control the selective Type 1 error --- that is, the probability of rejecting the null hypothesis, provided that the null hypothesis holds, and given that we decided to test this null hypothesis.
While double dipping is pervasive throughout many application areas, in this talk I'll focus on the analysis of single-cell RNA-sequencing data. I'll introduce count splitting, an approach to overcome issues associated with double dipping in the context of latent variable estimation for count-valued data.
Повторяем попытку...
Доступные форматы для скачивания:
Скачать видео
-
Информация по загрузке:
