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Mitigating the effect of measurement errors in quantile estimation

Author

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  • Schechtman, E.
  • Spiegelman, C.

Abstract

Quantiles are frequently used as descriptive measures. When data contains measurement errors, using the contaminated data to estimate the quantiles results in biased estimates. In this paper, we suggest two methods for reducing the effect of measurement errors on the quantile estimates and compare them, via an extensive simulation study, to the estimates obtained by the naive method, that is: by the estimates obtained from the observed (contaminated) data. The method we recommend is based on a method in a paper by Cook and Stefanski. However, we suggest using a combination of bootstrap and jackknifing to replace their extrapolation step.

Suggested Citation

  • Schechtman, E. & Spiegelman, C., 2007. "Mitigating the effect of measurement errors in quantile estimation," Statistics & Probability Letters, Elsevier, vol. 77(5), pages 514-524, March.
    • Handle: RePEc:eee:stapro:v:77:y:2007:i:5:p:514-524
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    Cited by:

    1. Karl Majeske & Terri Lynch-Caris & Janet Brelin-Fornari, 2010. "Quantifying R2 bias in the presence of measurement error," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(4), pages 667-677.

    More about this item

    Keywords

    Bootstrap Jackknife Percentiles;

    Statistics

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