Special
Issue of Computational Statistics and Data Analysis
Matrix Computations and Statistics II
A
number of interesting and important ideas have resulted from the relationship
between matrix computations and statistics. Well known examples include the
solution of least squares problems, computation of the singular value decomposition
and its generalizations, estimation of principal components, computation of
canonical correlations, several cluster analysis algorithms, and the solution
of total least squares problems.
A
previous special issue on this area featured papers on multidimensional
scaling, an application to web search engines, an algorithm for seemingly
unrelated regression models, an error measurement model for motion analysis, and
a survey on alternating least squares problems. These papers left the
impression that the overlap between
matrix
computations and statistics is a fertile area of research.
Thus we
propose a second special issue on matrix computations and statistics. The
editors would like to receive papers on any of the topics listed above and also topics such as latent
semantic indexing, structured total least squares, cluster analysis, complete
orthogonal decompositions, data compression, linear discriminant analysis, dimension
reduction/feature extraction, and applications of statistical matrix computing
to other scientific disciplines.
The
editors for this special issue will be
Jesse
L. Barlow
Department
of Computer Science
and
Engineering
The
Pennsylvania State University
University
Park, PA 16802-6106
Patrick
J.F. Groenen
Econometric
Institute
Erasmus
University Rotterdam
Room
H11.23
P.O.
Box 1738
3000 DR
Rotterdam, The Netherlands
Haesun
Park
4-192
EE/CS
Department
of Computer Science
and
Engineering
University
of Minnesota
Minneapolis,
MN 55455
Hongyuan
Zha
Department
of Computer Science
and
Engineering
The
Pennsylvania State University
University
Park, PA 16802-6106
The
deadline for submission to this special issue is December 1, 2003.
Manuscripts
submitted to this special issue will be refereed according to
standard
procedures for Computational Statistics and Data Analysis.