Random weighting estimation of parameters in generalized Gaussian distribution

Authors:
SheSheng Gao;Zhihua Feng;Yongmin Zhong;Bijan Shirinzadeh
Affiliations:
School of Automatics, Northwestern Polytechnical University, Xi'an 710072, China;School of Automatics, Northwestern Polytechnical University, Xi'an 710072, China and The Second Academy, China Aerospace Science and Industry Corporation, Beijing 100854, China;Department of Mechanical Engineering, Monash University, Vic. 3800, Australia;Department of Mechanical Engineering, Monash University, Vic. 3800, Australia
Venue:
Information Sciences: an International Journal
Year:
2008

Citing 3
Cited 3

Law of large numbers for sample mean of random weighting estimate

Information Sciences: an International Journal
The random weighting estimate of quantile process

Information Sciences—Informatics and Computer Science: An International Journal
Convergence rate of empirical Bayes estimation for two-dimensional truncation parameters under linex loss

Information Sciences—Informatics and Computer Science: An International Journal

Random weighting estimation for fusion of multi-dimensional position data

Information Sciences: an International Journal
Random Weighting Estimation of One-sided Confidence Intervals in Discrete Distributions

International Journal of Intelligent Mechatronics and Robotics
Weak convergence for random weighting estimation of smoothed quantile processes

Information Sciences: an International Journal

Quantified Score

Hi-index	0.07

Visualization

Abstract

This paper studies random weighting estimation of shape and scale parameters in generalized Gaussian distribution (GGD). An expression is established to describe the relationship between moments and parameters. The strong convergence for random weighting estimation of GGD parameters is also rigorously proved. Computational simulations and practical experiments are presented to demonstrate the efficacy for random weighting estimation of GGD parameters.