JSM 2012 Home

JSM 2012 Online Program

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

Online Program Home

Abstract Details

Activity Number: 625
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Survey Research Methods
Abstract - #305429
Title: Model-Assisted Lasso Regression Estimator
Author(s): Kelly McConville*+ and Jay Breidt and Thomas Lee
Companies: Whitman College and Colorado State University and University of California at Davis
Address: 122 Otis Street, Walla Walla, WA, 99362-2076, United States
Keywords: complex surveys ; model selection ; lasso
Abstract:

In natural resource surveys, a substantial amount of auxiliary information, typically derived from remotely-sensed imagery and organized in the form of spatial layers in a geographic information system (GIS), is available. Some of this ancillary data may be extraneous and a sparse model would be appropriate. Model selection methods are therefore warranted. The `least absolute shrinkage and selection operator' (lasso) conducts model selection and parameter estimation simultaneously by penalizing the sum of the absolute values of the model coefficients. A survey-weighted lasso criterion, which accounts for the sampling design, is derived and a survey-weighted lasso estimator is presented. The root-n design consistency of the estimator and a central limit theorem result are proved. Several variants of the survey-weighted lasso estimator are constructed. In particular, a calibration estimator and a ridge regression approximation estimator are constructed to produce weights that can be applied to several study variables. Simulation studies show the lasso estimators are more efficient than the regression estimator when the true model is sparse.


The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2012 program




2012 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.