Publications des institutions partenaires
High Breakdown Inference in the Mixed Linear Model
Mixed linear models are used to analyse data in many settings. These models have in most cases a multivariate normal formulation. The maximum likelihood estimator (MLE) or the residual MLE (REML) are usually chosen to estimate the parameters. However, the latter are based on the strong assumption of exact multivariate normality. Welsh and Richardson (1997) have shown that these…
Institution partenaire
English / 01/01/2003
Fast Algorithms for Computing High Breakdown Covariance Matrices with Missing Data
Robust estimation of covariance matrices when some of the data at hand are missing is an important problem. It has been studied by Little and Smith (1987) and more recently by Cheng and Victoria-Feser (2002). The latter propose the use of high breakdown estimators and so-called hybrid algorithms (see e.g. Woodruff and Rocke 1994). In particular, the minimum volume ellipsoid of…
Institution partenaire
English / 01/01/2003
Robust Mean-Variance Portfolio Selection
This paper investigates model risk issues in the context of mean-variance portfolio selection. We analytically and numerically show that, under model misspecification, the use of statistically robust estimates instead of the widely used classical sample mean and covariance is highly beneficial for the stability properties of the mean-variance optimal portfolios. Moreover, we perform…
Institution partenaire
English / 01/01/2003
Degrees-of-freedom tests for smoothing splines
When using smoothing splines to estimate a function, the user faces the problem of choosing the smoothing parameter. Several techniques are available for selecting this parameter according to certain optimality criteria. Here, we take a different point of view and we propose a technique for choosing between two alternatives, for example allowing for two different levels of degrees of…
Institution partenaire
English / 01/01/2002
Welfare Rankings in the Presence of Contaminated Data
Stochastic dominance criteria are commonly used to draw welfare-theoretic inferences about comparisons of income distribution as well as ranking probability distributions in the analysis of choice under uncertainty. However, just as some measures of location and dispersion can be catastrophically sensitive to extreme values in the data it is also possible that conclusions drawn from…
Institution partenaire
English / 01/01/2002
High Breakdown Estimation of Multivariate Location and Scale With Missing Observations
In this paper, we consider the problem of outliers in incomplete multivariate data, when the aim is to estimate a measure of mean and covariance as it is the case for example in factor analysis. In such a situation the ER algorithm of Little and Smith (1987) which combines the EM algorithm for missing data and a robust estimation step based on an Mestimator could be used. However,…
Institution partenaire
English / 01/01/2002
Robust Inference with Binary Data
In this paper robustness properties of the maximum likelihood estimator (MLE) and several robust estimators for the logistic regression model when the responses are binary are analysed. It is found that the MLE and the classical Rao's score test can be misleading in the presence of model misspecification which in the context of logistic regression means either misclassification…
Institution partenaire
English / 01/01/2002
Robust Estimation and Inference for Generalised Latent Trait Models
The paper discusses the effect of model deviations such as data contamination on the maximum likelihood estimator (MLE) for a general class of latent trait models (citeNP{MoKn:00}). This is done with the use of the influence function (Hampel 1968, 1974) a mathematical tool to assess the robustness properties of any statistic, such as an estimator. Simulation studies show that the MLE…
Institution partenaire
English / 01/01/2002
Robust Lorenz Curves: A Semi-Parametric Approach
Lorenz curves and second-order dominance criteria are known to be sensitive to data contamination in the right tail of the distribution. We propose two ways of dealing with the problem: (1) Estimate Lorenz curves using parametric models for income distributions, and (2) Combine empirical estimation with a parametric (robust) estimation of the upper tail of the distribution using the…
Institution partenaire
English / 01/01/2001
Resistant Selection of the Smoothing Parameter for Smoothing Splines
Robust automatic selection techniques for the smoothing parameter of a smoothing spline are introduced. They are based on a robust predictive error criterion and can be viewed as robust versions of C p and cross-validation. They lead to smoothing splines which are stable and reliable in terms of mean squared error over a large spectrum of model distributions.
Institution partenaire
English / 01/01/2001
Robust Inference for Generalized Linear Models
By starting from a natural class of robust estimators for generalized linear models based on the notion of quasi-likelihood, we define robust deviances that can be used for stepwise model selection as in the classical framework. We derive the asymptotic distribution of tests based on robust deviances, and we investigate the stability of their asymptotic level under contamination. The…
Institution partenaire
English / 01/01/2001
Putting Robust Statistical Methods into Practice: Poverty Analysis in Tunisia
Poverty analysis often results in the computation of poverty indexes based on so-called poverty lines which can be region speci…c poverty lines. The poverty lines are made of two components, namely the amount of income to satisfy the food and the non food needs. For both components, one needs to estimate quantities such as local prices or the consummers' average basket, and this…
Institution partenaire
English / 01/01/2001
Distributional Dominance with Dirty Data
Distributional dominance criteria are commonly applied to draw welfare inferences about comparisons, but conclusions drawn from empirical implementations of dominance criteria may be inßuenced by data contamination. We examine a non-parametric approach to reÞning Lorenz-type comparisons and apply the technique to two important examples from the LIS data-base.
Institution partenaire
English / 01/01/2001
A General Robust Approach to the Analysis of Income Distribution, Inequality and Poverty
Income distribution embeds a large field of research subjects in economics. It is important to study how incomes are distributed among the members of a population in order for example to determine tax policies for redistribution to decrease inequality, or to implement social policies to reduce poverty. The available data come mostly from surveys (and not censuses as it is often…
Institution partenaire
English / 01/01/2000
Robust Logistic Regression for Binomial Responses
In this paper robustness properties of the maximum likelihood estimator (MLE) and several robust estimators for the logistic regression model when the responses are binary are analysed analytically by means of the Influence Function (IF) and empirically by means of simulations. It is found that the MLE and the classical Rao's score test can be misleading in the presence of model…
Institution partenaire
English / 01/01/2000
Robust Income Estimation with Missing Data
With income distributions it is common to encounter the problem of missing data. When a parametric model is fitted to the data, the problem can be overcome by specifying the marginal distribution of the observed data. With classical methods of estimation such as the maximum likelihood (ML) an estimator of the parameters can be obtained in a straightforward manner. Unfortunately, it…
Institution partenaire
English / 01/01/2000
Distributional Analysis: a Robust Approach
Distributional dominance criteria are commonly applied to draw welfare inferences about comparisons, but conclusions drawn from empirical implementations of dominance criteria may be influenced by data contamination. We show the conditions under which this may occur and propose empirical methods to work round the proble using both non-parametric and parametric approaches.
Institution partenaire
English / 01/01/2000
Comment on Giorgi's chapter: The Sampling Properties of Inequality Indices
Institution partenaire
English / 01/01/1999
Statistical Inference for Welfare under Complete and Incomplete Information
We show how a collection of results in the literature on the empirical estimation of welfare indicators from sample data can be unified. We also demonstrate how some of these ideas can be extended to empirically important cases where the data have been trimmed or censored.
Institution partenaire
English / 01/01/1999
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