JSM 2015 Preliminary Program

Online Program Home
My Program

Abstract Details

Activity Number: 651
Type: Contributed
Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #317336
Title: A Matrix-Variate Approach to Efficient Mean Estimation with Dependent Observations
Author(s): Michael Hornstein* and Kerby Shedden and Shuheng Zhou
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: dependent data ; Kronecker product ; correlated samples ; differential expression ; likelihood ratio statistic ; transposable data
Abstract:

Large-scale inference involves estimating many parameters simultaneously, for example estimating group-wise differences in mean expression levels of thousands of genes. Genomics researchers have claimed that correlations among individuals may be present in such data, due to batch effects or latent variables, violating the traditional independent samples framework. Such correlations change the distribution of test statistics, leading to incorrect assessments of differential expression. In the setting of two-group hypothesis testing with correlated rows and columns, Allen and Tibshirani proposed a matrix-variate model in which the covariances have Kronecker product structure. Under this model, we propose a likelihood-based method for more accurate estimation of the mean structure. We assess the performance of the approach using simulations, and we apply our method to data from two genomic studies, one with only a few correlated samples, and one with heavier dependencies due to batch effects.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2015 program





For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home