The Annals of Statistics

Asymptotic optimality of data-driven Neyman's tests for uniformity

Tadeusz Inglot and Teresa Ledwina

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Abstract

Data-driven Neyman's tests resulting from a combination of eyman' smooth tests for uniformity and Schwarz's selection procedure are nvestigated. Asymptotic intermediate efficiency of those tests with respect to the Neyman-Pearson test is shown to be 1 for a large set of converging alternatives. The result shows that data-driven Neyman's tests, contrary to classical goodness-of-it tests, are indeed omnibus tests adapting well to the data at hand.

Article information

Source
Ann. Statist., Volume 24, Number 5 (1996), 1982-2019.

Dates
First available in Project Euclid: 20 November 2003

Permanent link to this document
https://projecteuclid.org/euclid.aos/1069362306

Digital Object Identifier
doi:10.1214/aos/1069362306

Mathematical Reviews number (MathSciNet)
MR1421157

Zentralblatt MATH identifier
0905.62044

Subjects
Primary: 62G10: Hypothesis testing 62G20: Asymptotic properties 62G05: Estimation 62A10

Keywords
Goodness of fit smooth test Schwarz's criterion exponential family efficiency log-density estimation minimum relative entropy estimation large deviations

Citation

Inglot, Tadeusz; Ledwina, Teresa. Asymptotic optimality of data-driven Neyman's tests for uniformity. Ann. Statist. 24 (1996), no. 5, 1982--2019. doi:10.1214/aos/1069362306. https://projecteuclid.org/euclid.aos/1069362306


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