Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions

We give a general unified method that can be used for $L_1$ {\em closeness testing} of a wide range of univariate structured distribution families. More specifically, we design a sample optimal and computationally efficient algorithm for testing the equivalence of two unknown (potentially arbitrary) univariate distributions under the $\mathcal{A}_k$-distance metric: Given sample access to distributions with density functions $p, q: I \to \mathbb{R}$, we want to distinguish between the cases that $p=q$ and $\|p-q\|_{\mathcal{A}_k} \ge \epsilon$ with probability at least $2/3$. We show that for any $k \ge 2, \epsilon>0$, the {\em optimal} sample complexity of the $\mathcal{A}_k$-closeness testing problem is $\Theta(\max\{ k^{4/5}/\epsilon^{6/5}, k^{1/2}/\epsilon^2 \})$. This is the first $o(k)$ sample algorithm for this problem, and yields new, simple $L_1$ closeness testers, in most cases with optimal sample complexity, for broad classes of structured distributions.