November 16, 2024
Mahmoud Afshari

Mahmoud Afshari

Academic Rank: Associate professor
Address: Mahmoud Afshari, Associate Professor. Department of Statistics, College of science Persian Gulf University, 7516913798, Iran E-mail:afshar.5050@gmail.com or afshar@pgu.ac.ir TEL:00989177125766
Degree: Ph.D in statistics
Phone: 07731223328
Faculty: Faculty of Intelligent Systems and Data Science

Research

Title Two new Bayesian-wavelet thresholds estimations of elliptical distribution parameters under non-linear exponential balanced loss
Type Article
Keywords
Admissible estimator; Generalized Bayes estimator; Minimax estimator; Non-linear exponential balanced-loss function; Shrinkage estimator; Stein’s unbiased risk estimator; Wavelet estimator
Journal COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
DOI https://doi.org/10.1080/03610918.2023.2245173
Researchers ziba botvandi (First researcher) , Mahmoud Afshari (Second researcher) , Hamid Karamikabir (Third researcher)

Abstract

The estimation of mean vector parameters is very important in elliptical and spherically models. Among different methods, the Bayesian and shrinkage estimation are interesting. In this paper, the estimation of p-dimensional location parameter for p-variate elliptical and spherical distributions under an asymmetric loss function is investigated. We find generalized Bayes estimator of location parameters for elliptical and spherical distributions. Also we show the minimaxity and admissibility of generalized Bayes estimator in class of spherical distribution: We introduce two new shrinkage soft wavelet threshold estimators based on Huang shrinkage wavelet estimator (empirical) and Stein’s unbiased risk estimator (SURE) for elliptical and spherical distributions under non-linear exponential-balanced loss function. At the end, we present a simulation study to test the validity of the class of proposed estimators and physicochemical properties of the tertiary structure data set that is given to test the efficiency of this estimators in denoising.