3,823 research outputs found
Global and Local Two-Sample Tests via Regression
Two-sample testing is a fundamental problem in statistics. Despite its long
history, there has been renewed interest in this problem with the advent of
high-dimensional and complex data. Specifically, in the machine learning
literature, there have been recent methodological developments such as
classification accuracy tests. The goal of this work is to present a regression
approach to comparing multivariate distributions of complex data. Depending on
the chosen regression model, our framework can efficiently handle different
types of variables and various structures in the data, with competitive power
under many practical scenarios. Whereas previous work has been largely limited
to global tests which conceal much of the local information, our approach
naturally leads to a local two-sample testing framework in which we identify
local differences between multivariate distributions with statistical
confidence. We demonstrate the efficacy of our approach both theoretically and
empirically, under some well-known parametric and nonparametric regression
methods. Our proposed methods are applied to simulated data as well as a
challenging astronomy data set to assess their practical usefulness
Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators
We provide finite-sample analysis of a general framework for using k-nearest
neighbor statistics to estimate functionals of a nonparametric continuous
probability density, including entropies and divergences. Rather than plugging
a consistent density estimate (which requires as the sample size
) into the functional of interest, the estimators we consider fix
k and perform a bias correction. This is more efficient computationally, and,
as we show in certain cases, statistically, leading to faster convergence
rates. Our framework unifies several previous estimators, for most of which
ours are the first finite sample guarantees.Comment: 16 pages, 0 figure
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