Maximum-likelihood symmetric α-stable parameter estimation

J.S. Bodenschatz; C.L. Nikias

doi:10.1109/78.757225

ScienceGate Book Chapters

JOURNAL ARTICLE

Maximum-likelihood symmetric α-stable parameter estimation

J.S. Bodenschatz C.L. Nikias

Year: 1999 Journal: IEEE Transactions on Signal Processing Vol: 47 (5)Pages: 1382-1384 Publisher: Institute of Electrical and Electronics Engineers

DOI: 10.1109/78.757225

Get Full-Text PDF Get Analytical Report

Abstract

Using the close relation between Fisher scoring and Newton maximization, and an efficient density function evaluation, we develop a fast maximum-likelihood parameter estimation method. Simulations show the algorithm to be superior in accuracy to McCulloch's (1986) method and to achieve the Cramer-Rao bound.

Keywords:

Estimation theory Maximum likelihood sequence estimation Mathematics Maximum likelihood Expectation–maximization algorithm Scoring algorithm Likelihood function Maximization Applied mathematics Cramér–Rao bound Upper and lower bounds Algorithm Statistics Mathematical optimization Newton's method Probability density function Mathematical analysis Nonlinear system

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.08

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Control Systems and Identification

Physical Sciences → Engineering → Control and Systems Engineering

Blind Source Separation Techniques

Physical Sciences → Computer Science → Signal Processing

Advanced Adaptive Filtering Techniques

Physical Sciences → Engineering → Computational Mechanics

Maximum-likelihood symmetric α-stable parameter estimation

Abstract

Metrics

Citation History

Topics

Related Documents

Maximum likelihood estimation for symmetric α-stable Ornstein–Uhlenbeck processes

Maximum likelihood estimation for α-stable autoregressive processes

Maximum Likelihood Parameter Estimation

Symmetric maximum kernel likelihood estimation

Parameter estimation — Maximum likelihood approaches