RECURSIVELY RE-WEIGHTED LEAST-SQUARES ESTIMATION IN REGRESSION MODELS WITH PARAMETERIZED VARIANCE (WedAmPO1)

Author(s) : Luc Pronzato (CNRS - UNSA, France) Andrej Pazman (Comenius University, Slovakia)

Abstract : We consider a nonlinear regression model with parameterized variance and compare several methods of estimation: the Weighted Least-Squares (WLS) estimator; the two-stage LS (TSLS) estimator, where the LS estimator obtained at the first stage is plugged into the variance function used for WLS estimation at the second stage; and finally the recursively re-weighted LS (RWLS) estimator, where the LS estimator obtained after k observations is plugged into the variance function to compute the k-th weight for WLS estimation. We draw special attention to RWLS estimation which can be implemented recursively} when the regression model in linear (even if the variance function is nonlinear), and is thus particularly attractive for signal processing applications.

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