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The large-sample performance of backwards variable elimination

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  • Peter Austin

Abstract

Prior studies have shown that automated variable selection results in models with substantially inflated estimates of the model R2, and that a large proportion of selected variables are truly noise variables. These earlier studies used simulated data sets whose sample sizes were at most 100. We used Monte Carlo simulations to examine the large-sample performance of backwards variable elimination. We found that in large samples, backwards variable elimination resulted in estimates of R2 that were at most marginally biased. However, even in large samples, backwards elimination tended to identify the correct regression model in a minority of the simulated data sets.

Suggested Citation

  • Peter Austin, 2008. "The large-sample performance of backwards variable elimination," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(12), pages 1355-1370.
    • Handle: RePEc:taf:japsta:v:35:y:2008:i:12:p:1355-1370
    DOI: 10.1080/02664760802382434
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    Cited by:

    1. Bowe, Anica G., 2015. "Exploring social and psycho-social factors that might help explain the Afro-Caribbean boy underachievement in England," International Journal of Educational Development, Elsevier, vol. 42(C), pages 73-84.
    2. Monica Novackova & Richard S.J. Tol, 2018. "Climate Change Awareness and Willingness to Pay for its Mitigation: Evidence from the UK," Working Paper Series 0318, Department of Economics, University of Sussex Business School.

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