It's a good definition for the MD, but for outliers identification MD is not
robust because of masking and swamping phenomena: outliers could have low MD
and high MD means not in each cases outliers. See eg Barnet V, Lewis T.
(1994). Outliers in statistical data. John Wiley and Sons, New-York.,
Rousseew PJ, Leroy AM. (1987). Robust regression and outlier detection. John
Wiley and Sons, New-York. or works about robust distances.
--
===================================
Dr SAULEAU Erik-A.
DIM
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Lorenzo Camprini <[EMAIL PROTECTED]> a �crit dans le message :
[EMAIL PROTECTED]
>
> Teo ha scritto nel messaggio...
>
> >Anyone knows in what consist the Mahanalobis distance??
> >I have to measure the distance between two histograms...
> >
>
> from the StatSoft website (Glossary):
>
> "Mahalanobis distance. One can think of the independent variables
> (in a regression equation) as defining a multidimensional space in which
> each observation can be plotted. Also, one can plot a point representing
> the means for all independent variables. This "mean point" in the
> multidimensional
> space is also called the centroid. The Mahalanobis distance is the
distance
> of a case from the centroid in the multidimensional space, defined by the
> correlated
> independent variables (if the independent variables are uncorrelated,
> it is the same as the simple Euclidean distance).
> Thus, this measure provides an indication of whether or not an observation
> is an outlier with respect to the independent variable values."
>
> Proper citation:
> StatSoft, Inc. (1999). Electronic Statistics Textbook. Tulsa, OK:
StatSoft.
> WEB: http://www.statsoft.com/textbook/stathome.html.
>
> Lorenzo Camprini
> =======================================
> computer programmer,
> technical assistant in electronics for computer science
> in a college-level school
> =======================================
> Email: [EMAIL PROTECTED]
>
>
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