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Proceedings Paper

Goodness-Of-Fit Testing In The Presence Of Nuisance Parameters With Applications To Feature Selection And Pattern Recognition In Digital Image Processing
Author(s): Nicholas A. Nechval
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Paper Abstract

The objective of this paper is to focus attention on a new practicable statistical approach to goodness-of-fit testing which is based on the notion of sufficiency and. provides an unified efficient approach to the problem of test construction in the presence of nuisance parameters. The general strategy of the above approach is to transform a set of random variables into a smaller set of independently and identically distributed uniform random variables on the interval (0,1)-i.i.d. U(0,1) under the null hypothesis HO. Under the alternative hypothesis this set of rv's will, in general, not be i.i.d. U(0,1). In other words, we replace the composite hypotheses by equivalent simple ones. Any statistic which measures a distance from uniformity in the transformed sample can be used as a test statistic. For instance, for this situation standard procedures of goodness-of-fit testing such as those based on Kolmogorov-Smirnov and Cramervon Mises statistics can be used. The obtained results are applicable to feature selection and pattern recognition. According to proposed approach, the best subset of feature measurements is the subset which maximizes the likelihood function of statistic that measures a distance from uniformity in the transformed sample. For the sake of illustrations the examples are given.

Paper Details

Date Published: 25 October 1988
PDF: 8 pages
Proc. SPIE 1001, Visual Communications and Image Processing '88: Third in a Series, (25 October 1988); doi: 10.1117/12.969015
Show Author Affiliations
Nicholas A. Nechval, Civil Aviation Engineers Institute (USSR)

Published in SPIE Proceedings Vol. 1001:
Visual Communications and Image Processing '88: Third in a Series
T. Russell Hsing, Editor(s)

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