Accession Number : ADA181683

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

Corporate Author : RICE UNIV HOUSTON TX DEPT OF ELECTRICAL AND COMPUTER ENGINEERING

Personal Author(s) : Pham,Trung T ; De Figueiredo,Rui J

PDF Url : ADA181683

Report Date : Jan 1987

Pagination or Media Count : 6

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.

Descriptors :   *MAXIMUM LIKELIHOOD ESTIMATION, DENSITY, GAUSSIAN QUADRATURE, BIAS, TRANSFER FUNCTIONS, PROBABILITY DENSITY FUNCTIONS, WHITE NOISE, ESTIMATES

Subject Categories : Theoretical Mathematics

Distribution Statement : APPROVED FOR PUBLIC RELEASE