A Non-Classical Parameterization for Density Estimation Using Sample Moments
This addresses a core issue in statistics and signal processing by improving density estimation methods, though it appears incremental as it builds on moment-based approaches.
The paper tackles the problem of density estimation by proposing a non-classical parametrization that eliminates the need for choosing feasible functions, using sample moments and the squared Hellinger distance, with simulation results validating its performance against prevailing methods.
Probability density estimation is a core problem of statistics and signal processing. Moment methods are an important means of density estimation, but they are generally strongly dependent on the choice of feasible functions, which severely affects the performance. In this paper, we propose a non-classical parametrization for density estimation using sample moments, which does not require the choice of such functions. The parametrization is induced by the squared Hellinger distance, and the solution of it, which is proved to exist and be unique subject to a simple prior that does not depend on data, and can be obtained by convex optimization. Statistical properties of the density estimator, together with an asymptotic error upper bound are proposed for the estimator by power moments. Applications of the proposed density estimator in signal processing tasks are given. Simulation results validate the performance of the estimator by a comparison to several prevailing methods. To the best of our knowledge, the proposed estimator is the first one in the literature for which the power moments up to an arbitrary even order exactly match the sample moments, while the true density is not assumed to fall within specific function classes.