Survey Methodology
A cautionary note on Clark Winsorization

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by Mary H. Mulry, Broderick E. Oliver, Stephen J. Kaputa and Katherine J. ThompsonNote 1

  • Release date: December 20, 2016


Winsorization procedures replace extreme values with less extreme values, effectively moving the original extreme values toward the center of the distribution. Winsorization therefore both detects and treats influential values. Mulry, Oliver and Kaputa (2014) compare the performance of the one-sided Winsorization method developed by Clark (1995) and described by Chambers, Kokic, Smith and Cruddas (2000) to the performance of M-estimation (Beaumont and Alavi 2004) in highly skewed business population data. One aspect of particular interest for methods that detect and treat influential values is the range of values designated as influential, called the detection region. The Clark Winsorization algorithm is easy to implement and can be extremely effective. However, the resultant detection region is highly dependent on the number of influential values in the sample, especially when the survey totals are expected to vary greatly by collection period. In this note, we examine the effect of the number and magnitude of influential values on the detection regions from Clark Winsorization using data simulated to realistically reflect the properties of the population for the Monthly Retail Trade Survey (MRTS) conducted by the U.S. Census Bureau. Estimates from the MRTS and other economic surveys are used in economic indicators, such as the Gross Domestic Product (GDP).

Key Words: Outlier; Masking; Monthly retail trade survey.

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How to cite

Mulry, M.H., Oliver, B.E., Kaputa, S.J. and Thompson, K.J. (2016). A cautionary note on Clark Winsorization. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 42, No. 2. Paper available at


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