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;

The breakdown behavior of the maximum likelihood estimator in the logistic regression model.

In this note we discuss the breakdown behavior of the maximum likelihood (ML) estimator in the logistic regression model. We formally prove that the ML-estimator never explodes to infinity, but rather breaks down to zero when adding severe outliers to a data set. An example confirms this behavior. (C) 2002 Published by Elsevier Science B.V.breakdown point; logistic regression; maximum likelihood; robust estimation; generalized linear-models; robustness; existence; fits;

Robust estimation for ordinal regression.

Ordinal regression is used for modelling an ordinal response variable as a function of some explanatory variables. The classical technique for estimating the unknown parameters of this model is Maximum Likelihood (ML). The lack of robustness of this estimator is formally shown by deriving its breakdown point and its influence function. To robustify the procedure, a weighting step is added to the Maximum Likelihood estimator, yielding an estimator with bounded influence function. We also show that the loss in efficiency due to the weighting step remains limited. A diagnostic plot based on the Weighted Maximum Likelihood estimator allows to detect outliers of different types in a single plot.Breakdown point; Diagnostic plot; Influence function; Ordinal regression; Weighted maximum likelihood; Robust distances;

The breakdown behavior of the maximum likelihood estimator in the logistic regression model.

Abstract: In this note we discuss the breakdown behavior of the Maximum Likelihood (ML) estimator in the logistic regression model. We formally prove that the ML-estimator never explodes to infinity, but rather breaks down to zero when adding severe outliers to a data set. Numerical experiments confirm this behavior. As a more robust alternative, a Weighted Maximum Likelihood (WML) estimator will be considered.Model; Data;

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

Model;

Sparse and Robust Factor Modelling

Factor construction methods are widely used to summarize a large panel of variables by means of a relatively small number of representative factors. We propose a novel factor construction procedure that enjoys the properties of robustness to outliers and of sparsity; that is, having relatively few nonzero factor loadings. Compared to more traditional factor construction methods, we find that this procedure leads to better interpretable factors and to a favorable forecasting performance, both in a Monte Carlo experiment and in two empirical applications to large data sets, one from macroeconomics and one from microeconomics

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;

The European Consumer: United In Diversity?

The ongoing unification which takes place on the European political scene, along with recent advances in consumer mobility and communication technology, raises the question whether the European Union can be treated as a single market to fully exploit the potential synergy effects from pan-European marketing strategies. Previous research, which mostly used domain-specific segmentation bases, has resulted in mixed conclusions. In this paper, a more general segmentation base is adopted, as we consider the homogeneity in the European countries’ Consumer Confidence Indicators. Moreover, rather than analyzing more traditional static similarity measures, we adopt the concepts of dynamic correlation and cohesion between countries. The short-run fluctuations in consumer confidence are found to be largely country specific. However, a myopic focus on these fluctuations may inspire management to adopt multicountry strategies, foregoing the potential longer-run benefits from more standardized marketing strategies. Indeed, the Consumer Confidence Indicators become much more homogeneous as the planning horizon is extended. However, this homogeneity is found to remain inversely related to the cultural, economic and geographic distances among the various Member States. Hence, pan-regional rather pan-European strategies are called for

Decomposing Granger Causality over the Spectrum

We develop a bivariate spectral Granger-causality test that can be applied at each individual frequency of the spectrum. The spectral approach to Granger causality has the distinct advantage that it allows to disentangle (potentially) di®erent Granger- causality relationships over di®erent time horizons. We illustrate the usefulness of the proposed approach in the context of the predictive value of European production expectation surveys

The Global Entry of New Pharmaceuticals: A Joint Investigation of Launch Window and Price

Research on the launch of new products in the international realm is scarce. The present paper is the first to document how launch window (difference in months between the first worldwide launch and the subsequent launch in a specific country) and launch price are interrelated and how regulation influences both launch window and launch price. The research context is the global - 50 countries worldwide - launch of 58 new ethical drugs across 29 therapeutic areas. We show that the fastest launch occurs when the launch price is moderately high and the highest launch price occurs at a launch window of 85 months. We find that the health regulator acts strategically in that the extent to which it delays the launch of a new drug increases with the price of the new drug. We also find that regulation overall increases the launch window, except for patent protection. Surprisingly, regulation does not directly impact launch price. The descriptive information on average launch window and launch price and the interconnection between launch window and launch price allows managers in ethical drug companies to build more informed decisions for international market entry. This study also provides public policy analysts with more quantitative evidence regarding launch window and launch price on a broad sample of countries and categories