By Topic

Constrained solutions in importance via robust statistics

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)
Orsak, Geoffrey C. ; Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA ; Aazhang, B.

The problem of estimating estimating expectations of functions of random vectors via simulation is investigated. Monte Carlo simulations, also known as simple averaging, have been used as a direct means of estimation. A technique known as importance sampling can be used to modify the simulation via weighted averaging in the hope that the estimate will converge more rapidly to the expected value than standard Monte Carlo simulations. A constrained optimal solution to the problem of minimizing the variance of the importance sampling estimator is derived. This is accomplished by finding the distribution which is closest to the unconstrained optimal solution in the Ali-Silvey sense (S. Ali et al., 1966). The solution from the constraint class is shown to be the least favorable density function in terms of Bayes risk against the optimal density function. Examples of constraint classes, which include ε-mixture, show that the constrained optimal solution can be made arbitrarily close to the optimal solution. Applications to estimating probability of error in communication systems are presented

Published in:

Information Theory, IEEE Transactions on  (Volume:37 ,  Issue: 2 )