Maximum Likelihood Estimation of a Class of Non-Gaussian Densities with Application to Deconvolution,

Abstract

This paper investigates in detail the properties of the maximum likelihood estimator of the generalized p-Gaussian (gpG) probability density function (pdf) from N independent identically distributed (iid) samples, especially in the context of the deconvolution problem under gpG white noise. The first part describes the properties of the estimator independently on the application. The second part obtains the solution of the above mentioned deconvolution problem as the solution of a minimum norm problem in an l sub p normed space. In the present paper, we show that such a minimum norm solution is the maximum likelihood estimate is unbiased, with the lower bound of the variance of the error equal to the Cramer Rao lower bound, and the upper bound derived from the concept of a generalized inverse.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1987
Accession Number
ADA181683

Entities

People

  • Rui J. De Figueiredo
  • Trung T. Pham

Organizations

  • Rice University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Computers
  • Engineering
  • Engineers
  • Estimators
  • Mathematics
  • Maximum Likelihood Estimation
  • Noise
  • Optimal Estimators
  • Probability
  • Probability Density Functions
  • Statistical Algorithms
  • White Noise

Fields of Study

  • Engineering
  • Mathematics

Readers

  • Linear Algebra
  • Statistical inference.

Technology Areas

  • Space
  • Space - Space Objects