Linear Models pp 229-245 | Cite as
Robust Regression
Chapter
Abstract
Consider the multivariate linear model
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.
$$
{Y_i} = X_i^\prime \beta + {E_i},\quad \quad i = 1, \ldots ,n,$$
(9.1)
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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© Springer Science+Business Media New York 1995