A Kernel Independence Test for Random Processes

2014-02-18 · Kacper Chwialkowski, Arthur Gretton

A new non parametric approach to the problem of testing the independence of two random process is developed. The test statistic is the Hilbert Schmidt Independence Criterion (HSIC), which was used previously in testing independence for i.i.d pairs of variables. The asymptotic behaviour of HSIC is established when computed from samples drawn from random processes. It is shown that earlier bootstrap procedures which worked in the i.i.d. case will fail for random processes, and an alternative consistent estimate of the p-values is proposed. Tests on artificial data and real-world Forex data indicate that the new test procedure discovers dependence which is missed by linear approaches, while the earlier bootstrap procedure returns an elevated number of false positives. The code is available online: https://github.com/kacperChwialkowski/HSIC .

📄 PDF Abstract BibTeX arXiv:1402.4501

Code (1)

kacperChwialkowski/HSIC 공식 구현

Metric on random dynamical systems with vector-valued reproducing kernel Hilbert spaces

2019-06-17 · Isao Ishikawa, Akinori Tanaka, Masahiro Ikeda, Yoshinobu Kawahara

Development of metrics for structural data-generating mechanisms is fundamental in machine learning and the related fields. In this paper, we give a general framework to construct metrics on random nonlinear dynamical sy…

A Wild Bootstrap for Degenerate Kernel Tests

2014-08-23 · NeurIPS 2014 12 · Kacper Chwialkowski, Dino Sejdinovic, Arthur Gretton

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 whi…

Benchmarking Time Series Time Series Analysis

Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes

2021-09-08 · NeurIPS 2021 12 · Cristopher Salvi, Maud Lemercier, Chong Liu, Blanka Hovarth 외

Stochastic processes are random variables with values in some space of paths. However, reducing a stochastic process to a path-valued random variable ignores its filtration, i.e. the flow of information carried by the pr…

Causal Discovery

Learning Deep Kernels for Non-Parametric Independence Testing

2024-09-10 · Nathaniel Xu, Feng Liu, Danica J. Sutherland

The Hilbert-Schmidt Independence Criterion (HSIC) is a powerful tool for nonparametric detection of dependence between random variables. It crucially depends, however, on the selection of reasonable kernels; commonly-use…

Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery

2017-02-13 · Eric V. Strobl, Kun Zhang, Shyam Visweswaran

Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing. The Kernel Conditional Independence Test (KCIT) is currently one of the most popular CI tests in the non…