Now showing items 1-5 of 5
Nonparametric density contour estimation
Estimation of the level sets for an unknown probability density is done with no specific assumed form for that density, that is, non-parametrically. Methods for tackling this problem are presented. Earlier research showed ...
Transform-domain modeling of nonGaussian and 1/f processes
Classical Gaussian, Markov, and Poisson models have played a vital role in the remarkable success of statistical signal processing. However, a host of signals---images, network traffic, financial times series, seismic ...
Local and superlinear convergence of structured secant methods from the convex class
In this thesis we develop a unified theory for establishing the local and q-superlinear convergence of secant methods which use updates from Broyden's convex class and have been modified to take advantage of the structure ...
A geometry for detection theory
The optimal detector for a binary detection problem under a variety of criteria is the likelihood ratio test. Despite this simple characterization of the detector, analytic performance analysis in most cases is difficult ...
Practical and effective methods of simulation based parameter estimation for multidimensional data
In 1983, Atkinson, Bartoszynski, Brown, and Thompson proposed a method of parameter estimation referred to as "simulation based estimation", or SIMEST. SIMEST is closely related to maximum likelihood, in that both methods ...