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A Simple Adaptation of Variable Selection Software for Regression Models to Select Variables in Nested Error Regression Models

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  • Yan Li

    (University of Maryland)

  • Partha Lahiri

    (University of Maryland)

Abstract

Data users often apply standard regression model selection criteria to select variables in nested error regression models, which are widely used in small area estimation. We demonstrate through a Monte Carlo simulation study that this practice may lead to selection of a non-optimal or incorrect model. To assist data users who wish to use standard regression software, we propose a transformation of the data so that transformed data follow a standard regression model. Thus, variable selection software available for the standard regression model can be directly applied to the transformed data. We illustrate our methodology using survey and satellite data for corn and soybeans in 12 Iowa counties.

Suggested Citation

  • Yan Li & Partha Lahiri, 2019. "A Simple Adaptation of Variable Selection Software for Regression Models to Select Variables in Nested Error Regression Models," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(2), pages 302-317, December.
    • Handle: RePEc:spr:sankhb:v:81:y:2019:i:2:d:10.1007_s13571-018-0161-6
    DOI: 10.1007/s13571-018-0161-6
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    References listed on IDEAS

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    1. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    2. Claeskens,Gerda & Hjort,Nils Lid, 2008. "Model Selection and Model Averaging," Cambridge Books, Cambridge University Press, number 9780521852258, October.
    3. Florin Vaida & Suzette Blanchard, 2005. "Conditional Akaike information for mixed-effects models," Biometrika, Biometrika Trust, vol. 92(2), pages 351-370, June.
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    Cited by:

    1. Cai Song & Rao J. N. K. & Dumitrescu Laura & Chatrchi Golshid, 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.
    2. Yan Li, 2020. "Discussion of "Small area estimation: its evolution in five decades", by Malay Ghosh," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 35-39, August.
    3. Merfeld, Joshua D. & Newhouse, David & Weber, Michael & Lahiri, Partha, 2022. "Combining Survey and Geospatial Data Can Significantly Improve Gender-Disaggregated Estimates of Labor Market Outcomes," IZA Discussion Papers 15390, Institute of Labor Economics (IZA).
    4. 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.
    5. Song Cai & J.N.K. Rao, 2022. "Selection of Auxiliary Variables for Three-Fold Linking Models in Small Area Estimation: A Simple and Effective Method," Stats, MDPI, vol. 5(1), pages 1-11, February.

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