M-estimator, and D-optimality model construction using orthogonal forward regression

Hong, X. ORCID: https://orcid.org/0000-0002-6832-2298 and Chen, S. (2005) M-estimator, and D-optimality model construction using orthogonal forward regression. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 35 (1). pp. 155-162. ISSN 1083-4419 doi: 10.1109/tsmcb.2004.839910

Abstract/Summary

This correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using D-optimality for model structure selection and is based on an M-estimators of parameter estimates. M-estimator is a classical robust parameter estimation technique to tackle bad data conditions such as outliers. Computationally, The M-estimator can be derived using an iterative reweighted least squares (IRLS) algorithm. D-optimality is a model structure robustness criterion in experimental design to tackle ill-conditioning in model Structure. The orthogonal forward regression (OFR), often based on the modified Gram-Schmidt procedure, is an efficient method incorporating structure selection and parameter estimation simultaneously. The basic idea of the proposed approach is to incorporate an IRLS inner loop into the modified Gram-Schmidt procedure. In this manner, the OFR algorithm for parsimonious model structure determination is extended to bad data conditions with improved performance via the derivation of parameter M-estimators with inherent robustness to outliers. Numerical examples are included to demonstrate the effectiveness of the proposed algorithm.