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Statistical Applications of a Metric on Subspaces to Satellite Meteorology
L. J. Crone and D. S. Crosby
Vol. 37, No. 3 (Aug., 1995), pp. 324-328
Published by: Taylor & Francis, Ltd. on behalf of American Statistical Association and American Society for Quality
Stable URL: http://www.jstor.org/stable/1269916
Page Count: 5
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In many large-dimensional multivariate problems, it is useful to reduce the number of variates. One method of reducing the number of dimensions is to project the original data onto a subspace. The statistical analysis is then carried out in this subspace. Principal-component regression is an example of such a technique. For these applications it is useful to have a measure of the distance between subspaces and to study the sampling stability of such subspaces. To solve these problems, we use a metric on subspaces and bootstrap techniques. The techniques are applied to seven-dimensional vectors of upwelling radiances from the current meteorological satellites. We study the subspaces spanned by the principal components based on a sample categorized by location and surface type.
Technometrics © 1995 American Statistical Association