Estimators of the multiple correlation coefficient: local robustness and confidence intervals.

Many robust regression estimators are defined by minimizing a measure of spread of the residuals. An accompanying R-2-measure, or multiple correlation coefficient, is then easily obtained. In this paper, local robustness properties of these robust R-2-coefficients axe investigated. It is also shown how confidence intervals for the population multiple correlation coefficient can be constructed in the case of multivariate normality.Cautionary note; High breakdown-point; Influence function; Intervals; Model; Multiple correlation coefficient; R-2-measure; Regression analysis; Residuals; Robustness; Squares regression;

Robustness versus efficiency for nonparametric correlation measures.

Nonparametric correlation measures at the Kendall and Spearman correlation are widely used in the behavioral sciences. These measures are often said to be robust, in the sense of being resistant to outlying observations. In this note we formally study their robustness by means of their influence functions. Since robustness of an estimator often comes at the price of a loss inprecision, we compute efficiencies at the normal model. A comparison with robust correlation measures derived from robust covariance matrices is made. We conclude that both Spearman and Kendall correlation measures combine good robustness properties with high efficiency.asymptotic variance; correlation; gross-error sensitivity; influence function; Kendall correlation; robustness; Spearman correlation;

Analyse canonique basée sur des estimateurs robustes de la matrice de covariance.

Model;

Bounded influence regression using high breakdown scatter matrices.

In this paper we estimate the parameters of a regression model using S-estimators of multivariate location and scatter. The approach is proven to be Fisher-consistent, and the influence functions are derived. The corresponding asymptotic variances are obtained and it is shown how they can be estimated in practice. A comparison with other recently proposed robust regression estimators is made.fisher-consistency; influence function; robust regression; s-estimators; scatter matrices; multivariate location; robust estimation; s-estimators; linear-regression; rank regression; covariance; squares; diagnostics; stability; efficiency;

Ion-Induced Dipole Interactions and Fragmentation Times : Cα\alpha -Cβ\beta Chromophore Bond Dissociation Channel

The fragmentation times corresponding to the loss of the chromophore (C$\alpha$-- C$\beta$ bond dissociation channel) after photoexcitation at 263 nm have been investigated for several small peptides containing tryptophan or tyrosine. For tryptophan-containing peptides, the aromatic chromophore is lost as an ionic fragment (m/z 130), and the fragmentation time increases with the mass of the neutral fragment. In contrast, for tyrosine-containing peptides the aromatic chromophore is always lost as a neutral fragment (mass = 107 amu) and the fragmentation time is found to be fast (\textless{}20 ns). These different behaviors are explained by the role of the postfragmentation interaction in the complex formed after the C$\alpha$--C$\beta$ bond cleavage

Robustness versus efficiency for nonparametric correlation measures

Nonparametric correlation measures at the Kendall and Spearman correlation are widely used in the behavioral sciences. These measures are often said to be robust, in the sense of being resistant to outlying observations. In this note we formally study their robustness by means of their influence functions. Since robustness of an estimator often comes at the price of a loss inprecision, we compute efficiencies at the normal model. A comparison with robust correlation measures derived from robust covariance matrices is made. We conclude that both Spearman and Kendall correlation measures combine good robustness properties with high efficiency.nrpages: 1-24status: publishe

Robust linear discriminant analysis using S-estimators

The authors consider a robust linear discriminant function based on high breakdown location and covariance matrix estimators. They derive influence functions for the estimators of the parameters of the discriminant function and for the associated classification error. The most B-robust estimator is determined within the class of multivariate S-estimators. This estimator, which minimizes the maximal influence that an outlier can have on the classification error, is also the most B-robust location S-estimator. A comparison of the most B-robust estimator with the more familiar Biweight S-estimator is made. R ESUM E Les auteurs etudient une fonction discriminante lineaire robuste basee sur des estimateurs de position et de matrice de covariance a haut point de rupture. Ils calculent les fonctions d'influence des estimateurs des parametres de la fonction discriminante et de l'erreur de classement associee. L'estimateur le plus B-robuste est determine au sein de la classe des S-estimateu..

Analyse canonique basée sur des estimateurs robustes de la matrice de covariance

status: publishe

Robust estimation of location and scale

info:eu-repo/semantics/publishe