Consistency and Monte Carlo Simulation of a Data Driven Version of Smooth Goodness-of-Fit Tests

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Kallenberg, Wilbert C.M. and Ledwina, Teresa (1995) Consistency and Monte Carlo Simulation of a Data Driven Version of Smooth Goodness-of-Fit Tests. Annals of Statistics, 23 (5). pp. 1594-1608. ISSN 0090-5364

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Abstract:The data driven method of selecting the number of components in Neyman's smooth test for uniformity, introduced by Ledwina, is extended. The resulting tests consist of a combination of Schwarz's Bayesian information criterion (BIC) procedure and smooth tests. The upper bound of the dimension of the exponential families in applying Schwarz's rule is allowed to grow with the number of observations to infinity. Simulation results show that the data driven version of Neyman's test performs very well for a wide range of alternatives and is competitive with other recently introduced (data driven) procedures. It is shown that the data driven smooth tests are consistent against essentially all alternatives. In proving consistency, new results on Schwarz's selection rule are derived, which may be of independent interest.
Item Type:Article
Copyright:© 1995 Institute of Mathematical Statistics
Faculty:
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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Link to this item:http://purl.utwente.nl/publications/70371
Official URL:http://dx.doi.org/10.1214/aos/1176324315
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