A sliced-Wasserstein and k-nearest-neighbor minimum-distance estimator for the distribution of random coefficients β is consistent with polynomial-in-dimension computation, while its diffusion and causal extensions remain heuristic.
Smoothed NPMLEs in nonparametric Poisson mixtures and beyond
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abstract
We discuss nonparametric mixing distribution estimation under the Gaussian-smoothed optimal transport (GOT) distance. It is shown that a recently formulated conjecture -- that the Poisson nonparametric maximum likelihood estimator can achieve root-$n$ rate of convergence under the GOT distance -- holds up to some logarithmic terms. We also establish the same conclusion for other minimum-distance estimators, and discuss mixture models beyond the Poisson.
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A sliced Wasserstein and diffusion approach to random coefficient models
A sliced-Wasserstein and k-nearest-neighbor minimum-distance estimator for the distribution of random coefficients β is consistent with polynomial-in-dimension computation, while its diffusion and causal extensions remain heuristic.