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Detecting and Treating Verified Influential Values in a Monthly Retail Trade Survey

Author

Listed:
  • Mulry Mary H.
  • Oliver Broderick E.
  • Kaputa Stephen J.

    (U.S. Census Bureau, Washington, DC 20233, U.S.A.)

Abstract

In survey data, an observation is considered influential if it is reported correctly and its weighted contribution has an excessive effect on a key estimate, such as an estimate of total or change. In previous research with data from the U.S. Monthly Retail Trade Survey (MRTS), two methods, Clark Winsorization and weighted M-estimation, have shown potential to detect and adjust influential observations. This article discusses results of the application of a simulation methodology that generates realistic population time-series data. The new strategy enables evaluating Clark Winsorization and weighted M-estimation over repeated samples and producing conditional and unconditional performance measures. The analyses consider several scenarios for the occurrence of influential observations in the MRTS and assess the performance of the two methods for estimates of total retail sales and month-to-month change.

Suggested Citation

  • 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 1-27, December.
    • Handle: RePEc:vrs:offsta:v:30:y:2014:i:4:p:27:n:8
    DOI: 10.2478/jos-2014-0045
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    Cited by:

    1. 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.
    2. Chauvet, Guillaume & Do Paco, Wilfried, 2018. "Exact balanced random imputation for sample survey data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 1-16.
    3. Helene Boistard & Guillaume Chauvet & David Haziza, 2016. "Doubly Robust Inference for the Distribution Function in the Presence of Missing Survey Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 683-699, September.
    4. Robert Graham Clark & Philip Kokic & Paul A. Smith, 2017. "A Comparison of two Robust Estimation Methods for Business Surveys," International Statistical Review, International Statistical Institute, vol. 85(2), pages 270-289, August.

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