Estimating probabilities from invariant permutation distributions
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Publication:3598299
DOI10.1007/BF02589042zbMath1446.62124MaRDI QIDQ3598299
Publication date: 3 February 2009
Published in: Journal of the Italian Statistical Society (Search for Journal in Brave)
bootstrapimportance samplingpermutation testsantithetic samplingcontrol variates samplingpermutation confidence intervals
Computational methods for problems pertaining to statistics (62-08) Nonparametric hypothesis testing (62G10) Nonparametric tolerance and confidence regions (62G15) Monte Carlo methods (65C05)
Cites Work
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- Most powerful invariant permutation tests
- Bootstrap and randomization tests of some nonparametric hypotheses
- Permutation tests. A practical guide to resampling methods for testing hypotheses
- Modified Randomization Tests for Nonparametric Hypotheses
- Approximation Theorems of Mathematical Statistics
- Testing variance equality with randmization tests
- A resampling procedure for nonparametric combination of several dependent tests
- On Obtaining Permutation Distributions in Polynomial Time
- Rerandomization Inference on Regression and Shift Effects: Computationally Feasible Methods
- On the theory of modified randomization tests for nonparametric hypotheses
- Importance Sampling for Bootstrap Confidence Intervals
- Importance sampling and the nested bootstrap
- Antithetic resampling for the bootstrap
- The Use of Control Variates in Monte Carlo Estimation of Power
- Significance levels and confidence intervals for permutation tests
- Distribution‐free confidence intervals
- On the Theory of Some Non-Parametric Hypotheses
- The Large-Sample Power of Tests Based on Permutations of Observations
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