Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Controlling the losing probability in a monotone game
Information Sciences: an International Journal
A general framework for learning rules from data
IEEE Transactions on Neural Networks
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We introduce a new concept of nonparametric test for statistically deciding if a model fits a sample of data well. The employed statistic is the empirical cumulative distribution e.c.d.f. of the measure of the blocks determined by the ordered sample. For any distribution law underlying the data this statistic is distributed around a Beta cumulative distribution law c.d.f. so that the shift between the two curves is the statistic at the basis of the test. Its distribution is computed through a new bootstrap procedure from a population of free parameters of the model that are compatible with the sampled data according to the model. Closing the loop, we may expect that if the model fits the data well the Beta c.d.f. constitutes a template for the block e.c.d.f.s that are compatible with the observed data. In the paper we show how to appreciate the template functionality in the case of a good fit and also how to discriminate bad models. We show the test's potential in comparison to conventional tests, both in case studies and in a well-known benchmark for the semiparametric logistic model used widely in database analysis.