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# Winsorising 1.0

Date Added: July 25, 2013  |  Visits: 226

Winsorising extreme values.Winsorising or Winsorization is the transformation of statistics by limiting extreme values in the statistical data to reduce the effect ofpossibly spurious outliers. It is named after the engineer-turned-biostatistician Charles P. Winsor (1895d-deOCt1951). The effect is the same as clipping in signal processing. The distribution of many statistics can be heavily influenced by outliers. A typical strategy is to set all outliers to a specified percentile of the data; for example, a 90% Winsorisation wouldsee all data below the 5th percentile set to the 5th percentile, and dataabove the 95th percentile set to the 95th percentile. Winsorised estimators are usually more robust to outliers than their more standard forms, although there are alternatives, such as trimming, that will achieve a similar effect. Note that Winsorizing is not equivalent to simply excluding data, which is a simpler procedure, called trimming. In a trimmed estimator, the extreme values are discarded; in a Winsorizedestimator, the extreme values are instead replaced by certain percentiles(the trimmed minimum and maximum).Thus a Winsorized mean is not the same as a truncated mean. For instance,the 5% trimmed mean is the average of the 5th to 95th percentile of thedata, while the 90% Winsorised mean sets the bottom 5% to the 5thpercentile, the top 5% to the 95th percentile, and then averages the data By itself, WINSORISING runs a demo Syntax: Y=winsorising(X,W)X - data matrix or vectorFor vectors, WINSORISING(X) is the winsorized X array.For matrices, WINSORISING(X) is a matrix containing the winsorized element from each column. W - Amount of winsoritazion (90 by default). If you set W=90 thismeans that the remaing 10% (0-5th percentile and 95-100th percentile) will be substituted.

 Requirements: No special requirements Platforms: Matlab Keyword: Average,  Bottom,  Instance,  Maximum,  Minimum,  Percentiles,  Replaced,  Truncated Users rating: 0/10