WINSORIZE
Name:
Type:
Purpose:
Description:
The computation of many statistics can be heavily influenced by extreme
values. One approach to providing a more robust computation of the
statistic is to Winsorized the data before computing the statistic.
To Winsorized the data, tail values are set equal to some specified
percentile of the data. For example, for a 90% Winsorization, the bottom
5% of the values are set equal to the value corresponding to the 5th
percentile while the upper 5% of the values are set equal to the value
corresponding to the 95th percentile.
Note that Winsorization is not equivalent to simply throwing some
of the data away. This is because the order statistics are not
independent.
Syntax:
LET <y2> = WINSORIZE <y1>
<SUBSET/EXCEPT/FOR qualification>
where <y1> is the response variable;
<y2> is a variable where the computed Winsorized values are
stored;
and where the <SUBSET/EXCEPT/FOR qualification> is optional.
Examples:
LET Y2 = WINSORIZE Y1
LET Y2 = WINSORIZE Y1 SUBSET TAG > 2
Note:
The analyst must specify the percentages to Winsorize in each tail.
This is done by defining the internal variables P1 (the lower
tail) and P2 (the upper tail). For example, to Winsorize 10% of
each tail, do the following:
LET P1 = 10
LET P2 = 10
LET Y2 = WINSORIZE Y1
Default:
Synonyms:
Related Commands:
WINSORIZED MEAN

= Compute a Winsorized mean.

WINSORIZED VARIANCE

= Compute the Winsorized variance.

WINSORIZED STANDARD DEVIATION

= Compute a Winsorized standard deviation.

WINSORIZED COVARIANCE

= Compute a Winsorized covariance.

WINSORIZED CORRELATION

= Compute a Winsorized correlation.

VARIANCE

= Compute the variance.

BIWEIGHT MIDVARIANCE

= Compute the biweight midvariance.

Applications:
Implementation Date:
Program:
LET Y1 = CAUCHY RANDOM NUMBERS FOR I = 1 1 100
LET P1 = 10
LET P2 = 10
LET Y2 = WINSORIZE Y1
LET A1 = EXTREME Y1
LET A2 = EXTREME Y2
Date created: 7/22/2002
Last updated: 4/4/2003
Please email comments on this WWW page to
alan.heckert@nist.gov.
