Welcome to the blind minimax estimation (BME) homepage. Here you can find usage examples and MATLAB implementations of the newly developed BME method. BME is a biased estimation technique for the linear regression problem, proved to dominate the commonly-used least squares estimator. In this website we describe both the simple one-shot BME and an extension for on-line systems which is designed to operate with low computation time.

This website is built as follows:

¨ One-shot BME introduces the blind minimax estimator, a technique dominating the least squares approach [1]. It contains an overview of the method and a Matlab example.

¨ On-line BME describes an application of the BME for adaptive estimation [3]. Here you will find Matlab code illustrating the improvement over the standard RLS technique.

¨ Links & Downloads contains BME publications, contact information, links, and a downloadable Matlab package with implementations of all estimators.  

"If one observes the real random variables X₁...X_n independently normally distributed with unknown means ξ₁...ξ_n and variance 1, it is customary to estimate ξ_i by X_i  . If the loss is the sum of squares of the errors, this estimator is admissible for n ≤ 2, but inadmissible for n ≥ 3."
C. Stein, 1956 [5]
(also known as Stein’s phenomenon)

This website is a part of an undergraduate project
by Guy Leibovitz and Asaf Elron, under the supervision of Zvika Ben-Haim,
at the Signal and Image Processing Lab, Faculty of Electrical Engineering,
Technion—Israel Institute of Technology.