Data-driven smooth tests when the hypothesis Is composite

Kallenberg, Wilbert C.M. and Ledwina, Teresa (1997) Data-driven smooth tests when the hypothesis Is composite. Journal of the American Statistical Association, 92 (439). pp. 1094-1104. ISSN 0162-1459

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Abstract:	In recent years several authors have recommended smooth tests for testing goodness of fit. However, the number of components in the smooth test statistic should be chosen well; otherwise, considerable loss of power may occur. Schwarz's selection rule provides one such good choice. Earlier results on simple null hypotheses are extended here to composite hypotheses, which tend to be of more practical interest. For general composite hypotheses, consistency of the data-driven smooth tests holds at essentially any alternative. Monte Carlo experiments on testing exponentiality and normality show that the data-driven version of Neyman's test compares well to other, even specialized, tests over a wide range of alternatives.
Item Type:	Article
Copyright:	© 1997 American Statistical Association
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Statistics and Probability (SP)
Link to this item:	http://purl.utwente.nl/publications/62408
Official URL:	http://www.jstor.org/stable/2965574
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