\<TITLE>Some thoughts about classification</TITLE>

<CENTER>
<H1> Some thoughts about classification</H1>
<H3>   Frank Hampel</H3>
<H3>   January 2002 </H3> 
</CENTER>

<H4> Abstract</H4>
The paper contains some general remarks on the high
art of data analysis, some philosophical thoughts about
classification, a partial review of outliers and robustness from
the point of view of applications, including a discussion of the
problem of model choice, and a review of several aspects of
robust estimation of covariance matrices, including the pragmatic
choice of a weight function based on empirical and theoretical
evidence. Several sections contain new (or at least original)
ideas: There are some proposals for incorporating robustness
into Bayesian practice and theory, including weighted log
likelihoods and Bayes' theorem for weighted data. Some small
ideas refer to artificial classification in a continuum,  to a
``robust'' (Prohorov-type) metric for high-dimensional data, and
to the use of multiple minimum spanning trees. A promising but
difficult research idea for clustering on the real line, based
on a new smoothing method, concludes the paper. 
<P>
<H4> Download: </H4>
<A HREF="102.ps.gz"> Compressed Postscript </A> (132 Kb)<br>
<A HREF="102.pdf">  PDF </A> (171 Kb)<br>


<P><HR><!---------------------------------------------------------------------->
Go back to the
 <A HREF="http://stat.ethz.ch/Research-Reports/"> Research Reports </A>
from
 <A HREF="http://www.stat.math.ethz.ch/"> Seminar für Statistik</A>.