Robustness in Data Analysis

General remarks; Huber minimax approach; Hampel approach; optimization criteria in data analysis - a probability-free approach; introductory remarks; translation and scale equivariant contrast functions; orthogonal equivariant contrast functions; monotonically equivariant contrast functions; minimal sensitivity to small perturbations in the data; affine equivariate contrast functions; robust mimimax estimation of location; introductory remarks; robust estimation of location in models with bounded variances; robust estimation of location in models with bounded subranges; robust estimators of multivariate location; least informative lattice distributions; robust estimation of scale; introductory remarks; measures of scale defined by functionals; M-, L-, and R-estimators of scale; Huber minimax estimator of scale; final remarks; robust regression and autoregression; introductory remarks; the minimax variance regression; robust autoregression; robust identification in dynamic models; final remarks; robustness of L1-norm estimators; introductory remarks; stability of L1-approximations; robustness of the L1-regression; final remarks; robust estimation of correlation; introductory remarks; analysis - Monte Carlo experiment; analysis - asymptotic characteristics; synthesis; minimax variance correlation; two-stage estimators - rejection of outliers plus classics; computation and data analysis technologies; introductory remarks on computation; adaptive robust procedures; smoothing quantile functions by the Bernstein polynomials; robust bivariate boxplots; applications; on robust elimination in the statistical theory of reliability; robust detection of signals based on optimisation criteria; statistical analysis of sudden cardiac death risk factors.