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Found 6 papers, 6 papers with code
Refined Convergence Rates for Maximum Likelihood Estimation under Finite Mixture Models
These new loss functions accurately capture the heterogeneity in convergence rates of fitted mixture components, and we use them to sharpen existing pointwise and uniform convergence rates in various classes of mixture models.
Plugin Estimation of Smooth Optimal Transport Maps
Our work also provides new bounds on the risk of corresponding plugin estimators for the quadratic Wasserstein distance, and we show how this problem relates to that of estimating optimal transport maps using stability arguments for smooth and strongly convex Brenier potentials.
Martingale Methods for Sequential Estimation of Convex Functionals and Divergences
We present a unified technique for sequential estimation of convex divergences between distributions, including integral probability metrics like the kernel maximum mean discrepancy, $\varphi$-divergences like the Kullback-Leibler divergence, and optimal transport costs, such as powers of Wasserstein distances.
Uniform Convergence Rates for Maximum Likelihood Estimation under Two-Component Gaussian Mixture Models
We derive uniform convergence rates for the maximum likelihood estimator and minimax lower bounds for parameter estimation in two-component location-scale Gaussian mixture models with unequal variances.
Estimating the Number of Components in Finite Mixture Models via the Group-Sort-Fuse Procedure
Estimation of the number of components (or order) of a finite mixture model is a long standing and challenging problem in statistics.
Minimax Confidence Intervals for the Sliced Wasserstein Distance
To motivate the choice of these classes, we also study minimax rates of estimating a distribution under the Sliced Wasserstein distance.
