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A robust alternative to the ratio estimator under non-normality

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  • Oral, Evrim
  • Oral, Ece

Abstract

In sampling theory, the traditional ratio estimator is the most common estimator of the population mean when the correlation between study and auxiliary variables is positively high. We introduce a new ratio-type estimator based on the order statistics of a simple random sample. We show that this new estimator is considerably more efficient than the traditional ratio estimator under non-normality, and remarkably robust to data anomalies such as presence of outliers in data sets.

Suggested Citation

  • Oral, Evrim & Oral, Ece, 2011. "A robust alternative to the ratio estimator under non-normality," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 930-936, August.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:8:p:930-936
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    Cited by:

    1. Sanjay Kumar & Shivanshu Kumar & Evrim Oral, 2021. "Robust Ratio- and Product-Type Estimators Under Non-normality via Linear Transformation Using Certain Known Population Parameters," Annals of Data Science, Springer, vol. 8(4), pages 733-753, December.

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