2,772 research outputs found
Multivariate data analysis: The French way
This paper presents exploratory techniques for multivariate data, many of
them well known to French statisticians and ecologists, but few well understood
in North American culture. We present the general framework of duality diagrams
which encompasses discriminant analysis, correspondence analysis and principal
components, and we show how this framework can be generalized to the regression
of graphs on covariates.Comment: Published in at http://dx.doi.org/10.1214/193940307000000455 the IMS
Collections (http://www.imstat.org/publications/imscollections.htm) by the
Institute of Mathematical Statistics (http://www.imstat.org
Sampling From A Manifold
We develop algorithms for sampling from a probability distribution on a
submanifold embedded in Rn. Applications are given to the evaluation of
algorithms in 'Topological Statistics'; to goodness of fit tests in exponential
families and to Neyman's smooth test. This article is partially expository,
giving an introduction to the tools of geometric measure theory
Threshold graph limits and random threshold graphs
We study the limit theory of large threshold graphs and apply this to a
variety of models for random threshold graphs. The results give a nice set of
examples for the emerging theory of graph limits.Comment: 47 pages, 8 figure
The duality diagram in data analysis: Examples of modern applications
Today's data-heavy research environment requires the integration of different
sources of information into structured data sets that can not be analyzed as
simple matrices. We introduce an old technique, known in the European data
analyses circles as the Duality Diagram Approach, put to new uses through the
use of a variety of metrics and ways of combining different diagrams together.
This issue of the Annals of Applied Statistics contains contemporary examples
of how this approach provides solutions to hard problems in data integration.
We present here the genesis of the technique and how it can be seen as a
precursor of the modern kernel based approaches.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS408 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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