Kernel-based tests for joint independence. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(1):5-31, 2017.Pfister, N., Bu¨hlmann, P., Scho¨lkopf, B. & Peters, J. (2016), `Kernel-based tests for joint independence'. URL: http://arxiv.org/abs/...
Kernel-based Tests for Joint Independence estimate of dHSIC, we define three different non-parametric hypothesis tests: a permutation test, a bootstrap test and a test based on a Gamma ... N Pfister,P Bühlmann,B Sch?Lkopf,... - 《Journal of the Royal Statistical Society》 被引量: 32...
This results in a Kernel-based Conditional Independence test (KCI-test). In this procedure we do not explicitly estimate the conditional or joint densities, nor discretize the conditioning variables. Our method is computationally appealing and is less sensitive to the dimensionality of Z compared to...
Also, entropy-based measures may capture joint multi-locus effects [61] or purely interactive effects (no influence of main effects) as is the case in [62]. Furthermore, most software tools producing entropy based estimators require complete data. For this reason, we included an additional ...
independentofthepastvalues. Wheneverthedependenceinonedirectionissignificantlyweakerthanintheother weinfertheformertobethetrueone. Bothapproacheswereabletodetectthedirectionofthetruegeneratingmodelfor simulateddatasets.Wealsoappliedourteststoalargenumberofrealworldtime series.TheARMAmethodmadeadecisionforasignifi...
Pfister, Niklas, Peter Buhlmann, Bernhard Scholkopf, and Jonas Peters. 2018. "Kernel-Based Tests for Joint Independence." Journal of the Royal Statistical Society Series B 80 (1): 5-31.Pfister, N., Bu¨hlmann, P., Scho¨lkopf, B. and Peters, J. (2018). Kernel-based tests for ...
Kernel-based Tests for Joint Independence We investigate the problem of testing whether $d$ random variables, which may or may not be continuous, are jointly (or mutually) independent. Our method b... N Pfister,P Bühlmann,B Sch?Lkopf,... - 《Journal of the Royal Statistical Society》 被...
This is important in many applications, for example in feature selection. The estimator is consistent, robust to outliers, and uses rank statistics only. We derive upper bounds on the convergence rate and propose independence tests too. We illustrate the theoretical contributions through a series of...
Two-sample and independence tests on stochastic processes have been widely studied in recent years. Under the stationarity assumption, ref. [8] investigate how the kernel cross-spectral density operator may be used to test for independence, and [9] formulate a wild bootstrap-based approach for ...
Two-sample and independence tests on stochastic processes have been widely studied in recent years. Under the stationarity assumption, ref. [8] investigate how the kernel cross-spectral density operator may be used to test for independence, and [9] formulate a wild bootstrap-based approach for ...