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Setting M-Estimation Parameters for Detection and Treatment of Influential Values

Author

Listed:
  • Mulry Mary H.

    (U.S. Census Bureau. 4600 Silver Hill Road, Washington, DC20233, U.S.A.)

  • Kaputa Stephen

    (U.S. Census Bureau. 4600 Silver Hill Road, Washington, DC20233, U.S.A.)

  • Thompson Katherine J.

    (U.S. Census Bureau. 4600 Silver Hill Road, Washington, DC20233, U.S.A.)

Abstract

Recent research on the use of M-estimation methodology for detecting and treating verified influential values in economic surveys found that initial parameter settings affect effectiveness. In this article, we explore the basic question of how to develop initial settings for the M-estimation parameters. The economic populations that we studied are highly skewed and are consequently highly stratified. While we investigated settings for several parameters, the most challenging problem was to develop an “automatic” data-driven method for setting the initial value of the tuning constant φ, the parameter with the greatest influence on performance of the algorithm. Of all the methods that we considered, we found that methods defined in terms of the accuracy of published estimates can be implemented on a large scale and yielded the best performance. We illustrate the methodology with an empirical analysis of 36 consecutive months of data from 19 industries in the Monthly Wholesale Trade Survey.

Suggested Citation

  • Mulry Mary H. & Kaputa Stephen & Thompson Katherine J., 2018. "Setting M-Estimation Parameters for Detection and Treatment of Influential Values," Journal of Official Statistics, Sciendo, vol. 34(2), pages 483-501, June.
    • Handle: RePEc:vrs:offsta:v:34:y:2018:i:2:p:483-501:n:10
    DOI: 10.2478/jos-2018-0022
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    References listed on IDEAS

    as
    1. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    2. J.-F. Beaumont & D. Haziza & A. Ruiz-Gazen, 2013. "A unified approach to robust estimation in finite population sampling," Biometrika, Biometrika Trust, vol. 100(3), pages 555-569.
    3. Mulry Mary H. & Oliver Broderick E. & Kaputa Stephen J., 2014. "Detecting and Treating Verified Influential Values in a Monthly Retail Trade Survey," Journal of Official Statistics, Sciendo, vol. 30(4), pages 721-747, December.
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