Goodness-of-Fit Testing for H\"older-Continuous Densities: Sharp Local Minimax Rates

TL;DR

This paper determines the optimal rates for goodness-of-fit testing of H"older-continuous densities in high-dimensional spaces, introducing new test statistics and a novel bulk-tail decomposition approach.

Contribution

It provides the first explicit characterization of local minimax rates for H"older densities, including novel test statistics and a bulk-tail splitting method.

Findings

Matching upper and lower bounds on testing rates are established.

New test statistics are proposed for density goodness-of-fit testing.

A novel bulk and tail decomposition of the space is introduced.

Abstract

We consider the goodness-of fit testing problem for H\"older smooth densities over $\mathbb{R}^d$: given $n$ iid observations with unknown density $p$ and given a known density $p_0$, we investigate how large $\rho$ should be to distinguish, with high probability, the case $p=p_0$ from the composite alternative of all H\"older-smooth densities $p$ such that $\|p-p_0\|_t \geq \rho$ where $t \in [1,2]$. The densities are assumed to be defined over $\mathbb{R}^d$ and to have H\"older smoothness parameter $\alpha>0$. In the present work, we solve the case $\alpha \leq 1$ and handle the case $\alpha>1$ using an additional technical restriction on the densities. We identify matching upper and lower bounds on the local minimax rates of testing, given explicitly in terms of $p_0$. We propose novel test statistics which we believe could be of independent interest. We also establish the first definition of an explicit cutoff $u_B$ allowing us to split $\mathbb{R}^d$ into a bulk part (defined as the subset of $\mathbb{R}^d$ where $p_0$ takes only values greater than or equal to $u_B$) and a tail part (defined as the complementary of the bulk), each part involving fundamentally different contributions to the local minimax rates of testing.