By Topic

Parametric probability density estimation based on an approximation by a discretized stochastic differential equation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Vesin, J.M. ; Lab. de Traitement des Signaux, Ecole Polytech. Federale de Lausanne, Switzerland ; Kunt, M.

The authors present a parametric probability density estimation technique for Markov processes defined by a first-order nonlinear autoregressive equation. It is based on the approximation of these processes as sampled versions of the continuous-time solutions of stochastic differential equations (SDEs) via the discretization scheme presented by T. Ozaki (1985). First, a polynomial estimate of the nonlinear recursion function is obtained from the data and then a suitable transformation of its coefficients is performed in order to obtain an estimate of the function in the corresponding SDE. The PDF estimate is then the equilibrium PDF of this SDE

Published in:

Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on

Date of Conference:

14-17 Apr 1991