American Statistical Association
New York City
Metropolitan Area Chapter

Memorial Sloan-Kettering Cancer Center
Biostatistics Seminar

Jim Booth
Department of Biological Statistics and Computational Biology
Cornell University


Modern biological data sets often consist of large numbers of predictors and small samples sizes. This fact has generated a vast literature on statistical methods for the so-called large p small n problem. Examples include gene expression data from micro-arrays, mass spectrometry (proteomic) data, and association studies involving a large number of genetic markers and a given phenotype. In this talk, I will discuss the use of three simple modeling and computational strategies for such problems: (1) random effects to induce shrinkage and for model parsimony; (2) mixtures for (empirical) Bayes prediction and classification; and (3) computation via the EM algorithm or MCMC, with the main focus being on a model we have developed for analyzing expression micro-arrays.

Date: Wednesday, June 2, 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.


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