Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wa...

‎Abstract: In this paper, a new mixture modelling using the normal mean-variance mixture of Lindley (NMVL) distribution has been considered. The proposed model is heavy-tailed and multimodal and can be used in dealing with asymmetric data in various theoretic and applied problems. We present a feasible computationally analytical EM algorithm for computing the maximum likelihood estimates. T...

Abstract Mixture of linear experts (MoE) model is one the widespread statistical frameworks for modeling, classification, and clustering data. Built on normality assumption error terms mathematical computational convenience, classical MoE has two challenges: (1) it sensitive to atypical observations outliers, (2) might produce misleading inferential results censored The aim then resolve these c...

In this paper, we propose a robust mixture regression model based on the skew scale mixtures of normal distributions (RMR-SSMN) which can accommodate asymmetric, heavy-tailed and contaminated data better. For variable selection problem, penalized likelihood approach with new combined penalty function balances SCAD l2 is proposed. The adjusted EM algorithm presented to get parameter estimates RM...