A Wild Bootstrap for Degenerate Kernel Tests

2014/08/23 by Kacper Chwialkowski, Chwialkowski, Kacper, Dino Sejdinović +3 · 5 citations
Computer Science · Economics, Econometrics and Finance · Mathematics · #62G10 #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Financial Risk and Volatility Modeling #Machine Learning (stat.ML) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1408.5404

openalex publication_date 2014/08/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed. This bootstrap method is used to construct provably consistent tests that apply to random processes, for which the naive permutation-based bootstrap fails. It applies to a large group of kernel tests based on V-statistics, which are degenerate under the null hypothesis, and non-degenerate elsewhere. To illustrate this approach, we construct a two-sample test, an instantaneous independence test and a multiple lag independence test for time series. In experiments, the wild bootstrap gives strong performance on synthetic examples, on audio data, and in performance benchmarking for the Gibbs sampler.

Citations

• Dependent wild bootstrap for degenerateU- andV-statistics

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