Department of Statistics Seminar
North Carolina State University
 
presents
 

Jianqing Fan
  Princeton University

Nonparametric estimation of genewise variance for Microarray Data




ABSTRACT

Estimation of genewise variance arises from two important applications of microarrays analysis: selecting differentially expressed genes and testing whether a microaray data has been properly normalized. A semiparametric model is introduced for estimating genewise variance, which involves vast nuisance parameters.  The problem itself poses significant challenges because the number of nuisance parameters is proportional to the sample size. In this study, we proposed a novel nonparametric estimator using within-array replications and estimated its asymptotical properties. The rates of convergence is established and demonstrated by simulation studies. The estimator also improves the power of the test of detecting statistically differentially expressed genes. The methodology is illustrated by the microarry data from MicroArray Quality Control (MAQC) project.

Friday, November 07, 2008

3:35-4:35 pm

321 Riddick

Refreshments will be served in the Riddick Reading Room at 3:00pm.  NOTE:  No food or drink is allowed in any of the classrooms in Riddick Hall.