A note on constrained M-estimation and its recursive analog in multivariate linear regression models |
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| Authors: | Calyampudi R Rao YueHua Wu |
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| Institution: | (1) Advanced Institute of Mathematics, Statistics and Computer Science, University of Hyderabad, Hyderabad, Andhra Pradesh, India;(2) Department of Mathematics and Statistics, York University, 4700 Keele Street, Toronto, Ontario, M3J 1P3, Canada |
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| Abstract: | In this paper, the constrained M-estimation of the regression coefficients and scatter parameters in a general multivariate
linear regression model is considered. Since the constrained M-estimation is not easy to compute, an up-dating recursion procedure
is proposed to simplify the computation of the estimators when a new observation is obtained. We show that, under mild conditions,
the recursion estimates are strongly consistent. In addition, the asymptotic normality of the recursive constrained M-estimators
of regression coefficients is established. A Monte Carlo simulation study of the recursion estimates is also provided. Besides,
robustness and asymptotic behavior of constrained M-estimators are briefly discussed.
The research was supported by the Natural Sciences and Engineering Research Council of Canada |
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| Keywords: | asymptotic normality breakdown point consistency constrained M-estimation influence function linear model M-estimation recursion estimation robust estimation |
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