M. Marzio

Regularized-Likelihood Deconvolution with Posterior-Density Reconstruction

Density deconvolution is an ill-posed inverse problem, as recovering the latent distribution amplifies high-frequency noise in the observed data. We propose a two-stage likelihood-based procedure. The first estimator maximizes the convolution likelihood over a regularized Gaussian-mixture sieve, while the second reuses...