This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 664
Type: Topic Contributed
Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #308195
Title: High-Dimensional Heteroscedastic Regression with Applications in eQTL Data Analysis
Author(s): Zhongyin John Daye*+ and Hongzhe Li
Companies: University of Pennsylvania and University of Pennsylvania
Address: 207 Blockley Hall, , Philadelphia, PA, 19104,
Keywords: High-dimensional data ; model selection ; motif analysis ; nonparametric regression ; sparsity ; structured covariates
Abstract:

We consider the problem of high-dimensional regression under non-constant error variances. Despite being a natural phenomenon in biological applications, heteroscedasticity has, so far, been largely ignored in the statistical genomics literature. In this paper, we propose a new methodology that allows non-constant error variances for high-dimensional estimation and model selection. We note that incorporating heteroscedasticity is an important step in improving statistical analysis of genomic and high-dimensional data, in general, where observations are often limited and of varying qualities. We provide simulation examples where heteroscedasticity arises from the presence of predictors explaining variability, outliers, and varying data sources. Further, we demonstrate the presence of heteroscedasticity in and apply our method to an expression quantitative trait loci (eQTLs) study, where gene expression levels are modeled. Our results suggest that the proposed method can improve variable selection and prediction for high-dimensional regression under heteroscedasticity.


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