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Limiting Behavior of RecursiveM-Estimators in Multivariate Linear Regression Models

โœ Scribed by B.Q. Miao; Y. Wu

Publisher
Elsevier Science
Year
1996
Tongue
English
Weight
865 KB
Volume
59
Category
Article
ISSN
0047-259X

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โœฆ Synopsis

In this paper, several recursive algorithms for computing M-estimates in multivariate linear regression models are discussed. It is shown that the recursive M-estimators of regression coefficient and scatter parameters are strongly consistent. In particular, the asymptotic normality of the recursive M-estimators of regression coefficients is established.

1996 Academic Press, Inc.

1. INTRODUCTION Consider the multivariate linear regression model

where X i , i=1, 2, ... are m_p matrices, ; is a p-vector of unknown regression coefficients, and e i , i=1, 2, ... are m_1 random errors. In the literature, there are many papers devoted to the theory of consistency and asymptotic normality for M-estimates of ;.

๐Ÿ“œ SIMILAR VOLUMES

Limiting Behavior of M-Estimators of Reg
Limiting Behavior of M-Estimators of Regression-Coefficients in High Dimensional Linear Models II. Scale-Invariant Case
โœ Z.D. Bai; Y. Wu ๐Ÿ“‚ Article ๐Ÿ“… 1994 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 317 KB

Asymptotics of M-estimators of the regression coefficients in linear models (both scale-variant and scale-invariant) when the number of regression coefficients tends to infinity as the sample size increases are investigated. The main purpose of this study is to establish the asymptotic properties un