Title :
A stochastic estimation algorithm with observation averaging
Author :
Juditsky, Anatoli
Author_Institution :
IRISA/INRIA, Rennes, France
fDate :
5/1/1993 12:00:00 AM
Abstract :
An algorithm for the constrained problem of estimating the regression coefficients is presented. The algorithm is based on the idea of direct averaging of the observations in order to estimate the search direction. It is shown that if the true parameter belongs to the permitted set, then the algorithm delivers asymptotically optimal estimates of the parameter. Finite convergence of the method is proved when the true parameter lies outside the permitted set
Keywords :
estimation theory; observability; parameter estimation; asymptotically optimal estimates; constrained problem; direct averaging; observation averaging; regression coefficients; search direction; stochastic estimation algorithm; true parameter; Algorithm design and analysis; Automatic control; Control systems; Filtering theory; Linear systems; MATLAB; Stochastic processes; Sufficient conditions; Vectors;
Journal_Title :
Automatic Control, IEEE Transactions on
