Date: 2018-10-12

Time: 16:00-

Location: CRM, Université de Montréal, Pavillon André-Aisenstadt, salle 6254

Abstract:

Many variables encountered in practice (e.g., economic variables) have skewed distributions. The latter provide a conducive ground for the presence of influential observations, which are those that have a drastic impact on the estimates if they were to be excluded from the sample. We examine the problem of influential observations in a classical statistic setting as well as in a finite population setting that includes two main frameworks: the design-based framework and the model-based framework. Within each setting, classical estimators may be highly unstable in the presence of influential units. We propose a robust estimator of the population mean based on the concept of conditional bias of a unit, which is a measure of influence. The idea is to reduce the impact of the sample units that have a large conditional bias. The proposed estimator depends on a cut-off value. We suggest selecting the cut-off value that minimizes the maximum absolute estimated conditional bias with respect to the robust estimator. The properties of the proposed estimator will be discussed. Finally, the results of a simulation study comparing the performance of several estimators in terms of bias and mean square error will be presented.

Speaker

David Haziza is a Full Professor in the department of Mathematics and Statistics at the university of Montreal. His research interests are in the theory and application of survey sampling. More specifically, he is interested in the inference in the presence of missing data, the inference in the presence of influential units, resampling methods and calibration.

Organized by the COLLOQUE DES SCIENCES MATHÉMATIQUES DU QUÉBEC

Seminar website: https://mcgillstat.github.io/