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Linear Models pp 229-245 | Cite as

Robust Regression

  • Calyampudi Radhakrishna Rao
  • Helge Toutenburg
Part of the Springer Series in Statistics book series (SSS)

Abstract

Consider the multivariate linear model
$$ {Y_i} = X_i^\prime \beta + {E_i},\quad \quad i = 1, \ldots ,n,$$
(9.1)
where Y i : p×1 is the observation on the ith individual, X i : q×p is the design matrix with known elements, β: q × 1 is a vector of unknown regression coefficients, and E i : p × 1 is the unobservable random error that is usually assumed to be suitably centered and to have a p-variate distribution. A central problem in linear models is estimating the regression vector β.

Keywords

Asymptotic Distribution Asymptotic Normality Moment Generate Function Strong Consistency Robust Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Calyampudi Radhakrishna Rao
    • 1
  • Helge Toutenburg
    • 2
  1. 1.Department of StatisticsThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Institut für StatistikUniversität MünchenMünchenGermany

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