Nonparametric MLE for Gaussian Location Mixtures: Certified Computation and Generic Behavior

Abstract: We study the nonparametric maximum likelihood estimator $\widehat{\pi}$ for Gaussian location mixtures in one dimension. It has been known since (Lindsay, 1983) that given an $n$-point dataset, this estimator always returns a mixture with at most $n$ components, and more recently (Wu-Polyanskiy, 2020) gave a sharp $O(\log n)$ bound for subgaussian data. In this work we study computational aspects of $\widehat{\pi}$. We provide an algorithm which for small enough $\varepsilon&gt;0$ computes an $\varepsilon$-approximation of $\widehat\pi$ in Wasserstein distance in time $K+Cnk<sup>2\log\log(1/\varepsilon)$. Here $K$ is data-dependent but independent of $\varepsilon$, while $C$ is an absolute constant and $k=|supp(\widehat{\pi})|\leq n$ is the number of atoms in $\widehat\pi$. We also certifiably compute the exact value of $|supp(\widehat\pi)|$ in finite time. These guarantees hold almost surely whenever the dataset $(x_1,\dots,x_n)\in [-cn<sup>{1/4},cn<sup>{1/4}]$ consists of independent points from a probability distribution with a density (relative to Lebesgue measure). We also show the distribution of $\widehat\pi$ conditioned to be $k$-atomic admits a density on the associated $2k-1$ dimensional parameter space for all $k\leq \sqrt{n}/3$, and almost sure locally linear convergence of the EM algorithm. One key tool is a classical Fourier analytic estimate for non-degenerate curves.