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Found 7 papers, 0 papers with code
USP: an independence test that improves on Pearson's chi-squared and the $G$-test
We present the $U$-Statistic Permutation (USP) test of independence in the context of discrete data displayed in a contingency table.
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms
We construct efficient randomized algorithms and test procedures, in both the case where only non-interactive privacy mechanisms are allowed and also in the case where all sequentially interactive privacy mechanisms are allowed.
Optimal rates for independence testing via $U$-statistic permutation tests
We study the problem of independence testing given independent and identically distributed pairs taking values in a $\sigma$-finite, separable measure space.
Efficient functional estimation and the super-oracle phenomenon
One interesting consequence of our results is the discovery that, for certain functionals, the worst-case performance of our estimator may improve on that of the natural `oracle' estimator, which is given access to the values of the unknown densities at the observations.
The conditional permutation test for independence while controlling for confounders
Like the conditional randomization test of Cand\`es et al. (2018), our test relies on the availability of an approximation to the distribution of $X \mid Z$.
Methodology Statistics Theory Statistics Theory
Nonparametric independence testing via mutual information
We propose a test of independence of two multivariate random vectors, given a sample from the underlying population.
Local nearest neighbour classification with applications to semi-supervised learning
We derive a new asymptotic expansion for the global excess risk of a local-$k$-nearest neighbour classifier, where the choice of $k$ may depend upon the test point.
