Data driven versions of neyman's test for uniformity based on bayesian rule
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Publication:2720216
DOI10.1080/00949650108812067zbMath1172.62304OpenAlexW2094306150MaRDI QIDQ2720216
Publication date: 26 June 2001
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949650108812067
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Cites Work
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- On the asymptotic power of the two-sided Kolmogorov-Smirnov test
- On the choice of a model to fit data from an exponential family
- Asymptotic comparison of Cramér-von Mises and nonparametric function estimation techniques for testing goodness-of-fit
- Asymptotic power properties of the Cramer-von Mises test under contiguous alternatives
- Estimating the dimension of a model
- Asymptotic optimality of data-driven Neyman's tests for uniformity
- Polynomial splines and their tensor products in extended linear modeling. (With discussions)
- Estimation of distributions using orthogonal expansions
- Consistency and Monte Carlo simulation of a data driven version of smooth goodness-of-fit tests
- Test of Significance Based on Wavelet Thresholding and Neyman's Truncation
- On stochastic complexity and nonparametric density estimation
- Power studies of tests for uniformity, II
- Exponential Models, Maximum Likelihood Estimation, and the Haar Condition
- Power studies of some tests for uniformity
- Data-Driven Version of Neyman's Smooth Test of Fit
- Testing goodness of fit via nonparametric function estimation techniques
- Data driven versions of pearson's chisquare test for uniformity
- Goodness-of-fit tests on a circle
- Asymptotic Theory of Certain "Goodness of Fit" Criteria Based on Stochastic Processes
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