Estimation of delta-contaminated density of the random intensity of Poisson data
Daniela De Canditiis, Marianna Pensky

TL;DR
This paper introduces a novel Lasso-based estimator for delta contaminated mixing densities of Poisson intensities, demonstrating improved accuracy and convergence through a two-stage iterative process and applying it to real Saturn's rings data.
Contribution
The paper develops a new Lasso-based estimation method for delta contaminated densities, with a novel optimization formulation and theoretical guarantees, outperforming existing approaches.
Findings
The estimator achieves a smaller error than minimax bounds.
Numerical simulations show advantages over Laguerre functions-based methods.
Application to Saturn's rings data demonstrates practical effectiveness.
Abstract
In the present paper, we constructed an estimator of a delta contaminated mixing density function of the intensity of the Poisson distribution. The estimator is based on an expansion of the continuous portion of the unknown pdf over an overcomplete dictionary with the recovery of the coefficients obtained as solution of an optimization problem with Lasso penalty. In order to apply Lasso technique in the, so called, prediction setting where it requires virtually no assumptions on dictionary and, moreover, to ensure fast convergence of Lasso estimator, we use a novel formulation of the optimization problem based on inversion of the dictionary elements. The total estimator of the delta contaminated mixing pdf is obtained using a two-stage iterative procedure. We formulate conditions on the dictionary and the unknown mixing density that yield a sharp…
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