Winsorising (Scripts) Publisher's description
from Giuseppe Cardillo
Winsorising extreme values
Winsorising extreme values.
Winsorising or Winsorization is the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of
possibly spurious outliers. It is named after the engineer-turned-biostatistician Charles P. Winsor (1895вЂ“1951). 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 would
see all data below the 5th percentile set to the 5th percentile, and data
above 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 Winsorized
estimator, 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 the
data, while the 90% Winsorised mean sets the bottom 5% to the 5th
percentile, the top 5% to the 95th percentile, and then averages the data
By itself, WINSORISING runs a demo
X - data matrix or vector
For 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 this
means that the remaing 10% (0-5th percentile and 95-100th percentile) will be substituted.
System Requirements:MATLAB 7.11 (2010b)
Program Release Status: New Release
Program Install Support: Install and Uninstall