Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 184 - New Research in Small Area Estimation
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Survey Research Methods Section
Abstract #311148
Title: A Variable Selection Method for Small Area Estimation Modeling of the Proficiency of Adult Competency
Author(s): Weijia Ren* and Jianzhu Li and Andreea Erciulescu and Tom Krenzke and Leyla Mohadjer
Companies: Westat and Westat and Westat and Westat and Westat
Keywords: cross-validation; multiple data sources; multivariate LASSO
Abstract:

In statistical modeling, it is crucial to have consistent covariates that are most relevant to the variable of interest in the model. With the increasing richness of data from multiple sources, the size of the pool of potential variables is escalating. Some variables, however, could provide redundant information, add noise to the estimation, or waste the degree of freedom in the model. Therefore, variable selection is needed as a parsimonious process that aims to identify a minimal set of predictors for maximum predictive power. This study illustrates the variable selection methods considered and used in the small area estimation (SAE) modeling of measures related to the proficiency of adult competency, constructed using survey data collected in the first cycle of PIAAC. The developed variable selection process consists of two phases: Phase 1 identifies a small set of variables that are consistently highly correlated with the outcomes through methods such as correlation matrix, and multivariate LASSO analysis; Phase 2 utilizes a k-fold cross-validation process to select a stable final set of variables to be used in the final SAE models.


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

Back to the full JSM 2020 program