PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Chen Y., So H. C., Kuruoglu E. E. Variance analysis of unbiased least lp-norm estimator in non-Gaussian noise. In: Signal Processing, vol. 122 pp. 190 - 203. Elsevier, 2016.
 
 
Abstract
(English)
Modeling time and space series in various areas of science and engineering require the values of parameters of interest to be estimated from the observed data. It is desirable to analyze the performance of estimators in an elegant manner without the need for extensive simulations and/or experiments. Among various performance measures, variance is the most basic one for unbiased estimators. In this paper, we focus on the estimator based on the â„"p-norm minimization in the presence of zero-mean symmetric non-Gaussian noise. Four representative noise models, namely, α-stable, generalized Gaussian, Student's t and Gaussian mixture processes, are investigated, and the corresponding variance expressions are derived for linear and nonlinear parameter estimation problems at pZ1. The optimal choice of p for different noise environments is studied, where the global optimality and sensitivity analyses are also provided. The developed formulas are verified by computer simulations and are compared with the Cramér-Rao lower bound.
URL: http://www.sciencedirect.com/science/article/pii/S0165168415004193
DOI: 10.1016/j.sigpro.2015.12.003
Subject Variance analysis
lp-norm estimation
Non-Gaussian noise
Impulsive noise
Alpha-stable noise
G.3 PROBABILITY AND STATISTICS. Distribution functions
G.3 PROBABILITY AND STATISTICS. Stochastic processes
62J10 Analysis of variance and covariance
60G52 Stable processes


Icona documento 1) Download Document PDF


Icona documento Open access Icona documento Restricted Icona documento Private

 


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional