Algorithms for Target Estimation using Stochastic Approximation
by Javier Cabrera and Inchi Hu
The target estimation algorithm was first applied in Cabrera,
Meer and Leung.(1994), and is designed to reduce bias and median bias.
In this paper, we present an algorithm for implementing the target
estimation method based on stochastic approximation. The application
of stochastic approximation algorithm to target estimation is especially
interesting because the bootstrap bias correction procedure
corresponds to the first iteration of our algorithm. This rather
surprising connection between bootstrap method and target estimation
as revealed by stochastic approximation algorithm suggests that when
the bias varies considerably as a function of the unknown parameter,
one can expect the target estimation to yield substantial improvement
over bootstrap bias correction. We also compare the performance of
the target estimation method with jackknife and bootstrap through
examples from autoregressive models and logistic regression models.
Bootstrap, Estimation Equation, Stochastic Approximation
READING THE ARTICLE: You can read the article in
portable document (.pdf) format (187243 bytes.)
NOTE: The content of this article is the intellectual property of the authors, who retains all rights to future publication.
This page has been accessed 2989 times since July 24, 2006.
Return to the Home Page.