Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/105252
Title: On constrained M-estimation and its recursive analog in multivariate linear regression models
Authors: Bai, Z. 
Chen, X.
Wu, Y.
Keywords: Asymptotic normality
Breakdown point
Consistency
Constrained M-estimation
Influence function
Linear model
M-estimation
Recursion estimation
Robust estimation
Issue Date: Apr-2008
Citation: Bai, Z.,Chen, X.,Wu, Y. (2008-04). On constrained M-estimation and its recursive analog in multivariate linear regression models. Statistica Sinica 18 (2) : 405-424. ScholarBank@NUS Repository.
Abstract: In this paper, the constrained M-estimation of the regression coefficients and scatter parameters in a multivariate linear regression model is considered. Robustness and asymptotic behavior are investigated. Since 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. A Monte Carlo simulation study of the recursion estimates is also provided.
Source Title: Statistica Sinica
URI: http://scholarbank.nus.edu.sg/handle/10635/105252
ISSN: 10170405
Appears in Collections:Staff Publications

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