American Statistical Association
New York City
Metropolitan Area Chapter

Memorial Sloan-Kettering Cancer Center
Biostatistics Seminar



Sijian Wang
Department of Statistics
University of Wisconsin - Madison

HIERARCHICALLY PENALIZED COX REGRESSION WITH GROUPED VARIABLES

In many biological and other scientific applications, predictors are often naturally grouped. For example, in biological applications, assayed genes or proteins are grouped by biological roles or biological pathways. When studying the dependence of survival outcome on these grouped predictors, it is desirable to select variables at both the group level and the within-group level. In this article, we develop a new method to address the group variable selection problem in the Cox proportional hazards model. Our method not only effectively removes unimportant groups, but also maintains the flexibility of selecting variables within the identified groups. We also show that the new method offers the potential for achieving the asymptotic oracle property.


Date: Wednesday, February 17, 2010
Time: 4:00 - 5:00 P.M.
Location: Memorial Sloan-Kettering Cancer Center
Department of Epidemiology and Biostatistics
307 East 63rd Street
(between First and Second Avenues)
Room 331
New York, New York
Note: To gain access to the building, please follow the directions by the telephone in the foyer.

RESERVATIONS ARE NOT REQUIRED


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