Robust Statistical Procedures: Second EditionHere is a brief, well-organized, and easy-to-follow introduction and overview of robust statistics. Huber focuses primarily on the important and clearly understood case of distribution robustness, where the shape of the true underlying distribution deviates slightly from the assumed model (usually the Gaussian law). An additional chapter on recent developments in robustness has been added and the reference list has been expanded and updated from the 1977 edition. |
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adaptive algorithms Analysis applied approach Approximation Assume assumptions asymptotic asymptotic variance bias bounded breakdown point calculate called classical computed consistent contained continuous convergence corresponding covariance defined density depend derivative designed determined deviations differentiable distribution efficiency Equations equivalent error estimate Example finite first Fisher given gives gross error Hampel Huber IC(x ideas independent influence curve influence function interesting invariant iteration least squares linear M-estimate Mathematical matrix maximum mean measures median Methods metric minimax minimizing neighborhoods normal Note Numerical observations obtain optimal orthogonal matrix outliers parameter particular performance possible probability problems procedures proofs properties random range regression residuals respect result robust sample sample mean scale sense simple situation small change solution standard statistics symmetric tests THEOREM theory topology transformations true underlying unique usually variables