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Data-Driven Smooth Tests When the Hypothesis Is Composite
Wilbert C. M. Kallenberg and Teresa Ledwina
Journal of the American Statistical Association
Vol. 92, No. 439 (Sep., 1997), pp. 1094-1104
Stable URL: http://www.jstor.org/stable/2965574
Page Count: 11
You can always find the topics here!Topics: Statistics, Simulations, Critical values, Approximation, Goodness of fit, P values, Statistical estimation, Null hypothesis, Random variables, Cosine function
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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.
Journal of the American Statistical Association © 1997 American Statistical Association