Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Polymorphism (biology) wikipedia , lookup
Minimal genome wikipedia , lookup
Epigenetics of human development wikipedia , lookup
Ridge (biology) wikipedia , lookup
Biology and consumer behaviour wikipedia , lookup
Gene expression programming wikipedia , lookup
Designer baby wikipedia , lookup
Gene expression profiling wikipedia , lookup
The Selfish Gene wikipedia , lookup
Department of Statistics STATISTICS COLLOQUIUM MICHAEL NEWTON Departments of Statistics and of Biostatistics and Medical Informatics University of Wisconsin-Madison Two Problems in High-Dimensional Statistics: A Specific One on the Analysis of Gene Function, and a General One on Ranking and Selection MONDAY, April 14, 2014, at 4:00 PM 133 Eckhart Hall, 5734 S. University Avenue Refreshments following the seminar in Eckhart 110 ABSTRACT I will investigate two inference problems. The data structure in the first case is a list of genes identified by some genome-wide assay, and the problem is to characterize the functional content of the list using known functional properties of all genes. By modeling the gene-list in terms of latent activities of biological functions, one obtains a compelling multivariate procedure to address the task. The available method ignores what turns out to be an important constraint in the highdimensional discrete parameter space. I will discuss this constraint and the inference improvements that follow when the constraint is respected. In the second part of the talk I will discuss the general problem of ranking and selection. Data form a collection of unit-specific point estimates coupled with unit-specific quantities that inform the uncertainty in the underlying unit-specific parameters (e.g., standard errors). When uncertainty varies substantially among units, different ranking/selection schemes can give very different inferences about the true ranking. I introduce and investigate a generic empirical Bayesian quantity—the r-value—which leads to rankings that are optimal in the sense of maximizing the joint probability that a unit’s parameter is among the top alpha-fraction of parameters and the unit is inferred to be in that top fraction. The gene-set problem is addressed in a paper with Zhishi Wang, Qiuling He, and Bret Larget. The ranking/selection findings are part of work with Nicholas Henderson. For further information and inquiries about building access for persons with disabilities, please contact Kirsten Wellman at 773.702.8333 or send her an email at [email protected]. If you wish to subscribe to our email list, please visit the following website: https://lists.uchicago.edu/web/arc/statseminars.