JSM 2015 Preliminary Program

Online Program Home
My Program

Abstract Details

Activity Number: 29
Type: Contributed
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #315857
Title: On the Use of Grouped Covariate Regression in Oversaturated Models
Author(s): Stephen Loftus* and Leanna House and Lisa Belden and Jeni Walke and Matt Becker
Companies: Virginia Tech and Virginia Tech and Virginia Tech and Virginia Tech and Virginia Tech
Keywords: Oversaturated Models ; Dimension Reduction ; Regression ; Bayesian ; Model Selection
Abstract:

In the age of big data, one problem of the utmost concern in statistical analyses is the oversaturated model, or where the number of covariates p exceeds the number of observations n. In this setting, simple statistical models can suffer myriad problems in accuracy and stability. To combat this problem, statisticians often either add constraints (e.g., LASSO) or inject prior information (e.g., Horseshoe prior).

We propose a new technique called Grouped Covariate Regression (GCR), a combination data mining and hierarchical modeling strategy that has two primary steps: (1) Group the variables and summarize, and (2) Model a response given the group summaries. There are several ways to implement each step, but for this paper a deterministic approach for grouping variables is considered along with an errors-in-variables model for assessing a response.

Simulation studies and an application to a biodiversity problem show many of the advantages of GCR. These advantages include (1) Overcoming the oversaturated model problem, (2) Lowering correlation between predictors in the model, (3) Overcoming issues presented by sparse matrices, and (4) Retaining interpretability for the coefficients.


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