American Statistical Association
New York City
Metropolitan Area Chapter

Memorial Sloan Kettering Cancer Center
Department of Epidemiology and Biostatistics
Biostatistics Seminar Series


Qi Long, Ph.D.
Professor, Department of Biostatistics, Epidemiology and Informatics
Director, Biostatistics Core, Abramson Cancer Center
University of Pennsylvania

KNOWLEDGE-GUIDED SCALABLE BAYESIAN VARIABLE SELECTION
FOR HIGH-DIMENSIONAL DATA

Variable selection for structured covariates lying on an underlying known graph is a problem motivated by practical applications, and has been a topic of increasing interest. However, most of the existing methods may not be scalable to high dimensional settings involving tens of thousands of variables lying on known pathways such as the case in genomics studies. We propose an adaptive Bayesian shrinkage approach which incorporates prior network information by smoothing the shrinkage parameters for connected variables in the graph, so that the corresponding coefficients have a similar degree of shrinkage. We fit our model via a computationally efficient expectation maximization algorithm which scalable to high dimensional settings. Theoretical properties for fixed as well as increasing dimensions are established, even when the number of variables increases faster than the sample size. We demonstrate the advantages of our approach in terms of variable selection, prediction, and computational scalability via a simulation study, and apply the method to a cancer genomics study.

Joint work with Changgee Chang and Suprateek Kundu.


Date: Wednesday, November 15, 2017
Time: 4:00 - 5:00 P.M.
Location: Memorial Sloan Kettering Cancer Center
Department of Epidemiology and Biostatistics
485 Lexington Avenue
(Between 46th & 47th Streets)
2nd Floor, Conference Room B
New York, New York

**Outside visitors please email celeat@mskcc.org for building access.
You must be on the security list to enter the floor.

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