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  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
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  3:15 p.m., Wednesday, October 31, 2007\\
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  Building 380, Room 380-C\\
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  \textsl{Susan Holmes} \\
  Statistics Department\\
  Stanford University
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  \subsection*{Statistics without Probability: the French Way}
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\noindent

I will present the geometric and perturbative approaches to multivariate statistics popularized in France in the mid-seventies. Inner products are central to this perspective, and I will show how they enable a unified treatment of principal components, multiple regression, discriminant analysis, kernel methods, correspondence analysis, multidimensional scaling, multivariate spatial analysis and conjoint analysis of a data cube. This geometric picture of data analysis is now very popular with the machine learning community. I will concentrate on some of the earlier tricks such as supplementary points and perturbation analysis that can be applied today to new problems in biology. 


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