14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper ThA2.2

Akcay, Huseyin (Anadolu Univ.), At, Nuray (Anadolu Univ.)

Convergence Analysis of Central and Minimax Algorithms in Scalar Regressor Models

Scheduled for presentation during the Regular Session "Identification of Linear Systems II" (ThA2), Thursday, March 30, 2006, 10:50−11:10, Banquet Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on July 22, 2018

Keywords Bounded Error Identification, Error Quantification


In this paper, estimation of a scalar parameter is considered with given lower and upper bounds of the scalar regressor. We derive non-asymptotic, lower and upper bounds on the convergence rates of the parameter estimate variances for noise probability density functions charecterized by a thin tail distribution. This presents an extension of the previous work for constant scalar regressors to arbitrary scalar regressors with magnitude constraints. We expect our results to stimulate further research interests in the statistical analysis of these set-based estimators when the unknown parameter is multi-dimensional and the probability distribution function of the noise is more general than the present setup.