21,317 research outputs found
Large-Scale Kernel Methods for Independence Testing
Representations of probability measures in reproducing kernel Hilbert spaces
provide a flexible framework for fully nonparametric hypothesis tests of
independence, which can capture any type of departure from independence,
including nonlinear associations and multivariate interactions. However, these
approaches come with an at least quadratic computational cost in the number of
observations, which can be prohibitive in many applications. Arguably, it is
exactly in such large-scale datasets that capturing any type of dependence is
of interest, so striking a favourable tradeoff between computational efficiency
and test performance for kernel independence tests would have a direct impact
on their applicability in practice. In this contribution, we provide an
extensive study of the use of large-scale kernel approximations in the context
of independence testing, contrasting block-based, Nystrom and random Fourier
feature approaches. Through a variety of synthetic data experiments, it is
demonstrated that our novel large scale methods give comparable performance
with existing methods whilst using significantly less computation time and
memory.Comment: 29 pages, 6 figure
Discussion of: Brownian distance covariance
Discussion on "Brownian distance covariance" by G\'{a}bor J. Sz\'{e}kely and
Maria L. Rizzo [arXiv:1010.0297]Comment: Published in at http://dx.doi.org/10.1214/09-AOAS312E the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
On a Nonparametric Notion of Residual and its Applications
Let be a continuous random vector in , . In this paper, we define the notion of a
nonparametric residual of on that is always independent of the
predictor . We study its properties and show that the proposed
notion of residual matches with the usual residual (error) in a multivariate
normal regression model. Given a random vector in
, we use this notion of
residual to show that the conditional independence between and , given
, is equivalent to the mutual independence of the residuals (of
on and on ) and . This result is used
to develop a test for conditional independence. We propose a bootstrap scheme
to approximate the critical value of this test. We compare the proposed test,
which is easily implementable, with some of the existing procedures through a
simulation study.Comment: 19 pages, 2 figure
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