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A Variable Selection Method for Small Area Estimation Modeling of the Proficiency of Adult Competency

Author

Listed:
  • Weijia Ren

    (Westat, 1600 Research Boulevard, Rockville, MD 20850, USA)

  • Jianzhu Li

    (Westat, 1600 Research Boulevard, Rockville, MD 20850, USA)

  • Andreea Erciulescu

    (Westat, 1600 Research Boulevard, Rockville, MD 20850, USA)

  • Tom Krenzke

    (Westat, 1600 Research Boulevard, Rockville, MD 20850, USA)

  • Leyla Mohadjer

    (Westat, 1600 Research Boulevard, Rockville, MD 20850, USA)

Abstract

In statistical modeling, it is crucial to have consistent variables that are the most relevant to the outcome variable(s) 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 degrees of freedom in the model. Therefore, variable selection is needed as a parsimonious process that aims to identify a minimal set of covariates for maximum predictive power. This study illustrated the variable selection methods considered and used in the small area estimation (SAE) modeling of measures related to the proficiency of adult competency that were constructed using survey data collected in the first cycle of the PIAAC. The developed variable selection process consisted of two phases: phase 1 identified a small set of variables that were consistently highly correlated with the outcomes through methods such as correlation matrix and multivariate LASSO analysis; phase 2 utilized a k-fold cross-validation process to select a final set of variables to be used in the final SAE models.

Suggested Citation

  • Weijia Ren & Jianzhu Li & Andreea Erciulescu & Tom Krenzke & Leyla Mohadjer, 2022. "A Variable Selection Method for Small Area Estimation Modeling of the Proficiency of Adult Competency," Stats, MDPI, vol. 5(3), pages 1-25, July.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:3:p:41-713:d:873206
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    References listed on IDEAS

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    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Song Cai & J. N. K. Rao & Laura Dumitrescu & Golshid Chatrchi, 2020. "Effective transformation-based variable selection under two-fold subarea models in small area estimation," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 68-83, August.
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