ONLINE - Bias in Parametric Estimation: Reduction and Useful Side-Effects

20.03.2020 11:15 – 12:15

RESEARCH CENTER FOR STATISTICS SEMINAR / ABSTRACT

This talk focuses on a unified theoretical and algorithmic framework for reducing bias in the estimation of statistical models from a practitioners point of view. The talk will briefly discuss how shortcomings of classical estimators can be overcome via reduction of bias, and provide a few illustrations for well-used statistical models with tractable likelihoods, including regression models with categorical responses and Beta regression. New results will then be presented on the use of bias reduction methods for linear mixed effects models by focusing on the typically small-sample setting of meta-regression in the presence of heterogeneity. The large effect that the bias of the variance components can have on inference motivates the application of the framework to deliver higher-order corrective methods for generalised linear mixed models, which typically have intractable likelihoods. The challenges in doing that will be presented along with resolutions.

Lieu

Zoom meeting room:
https://unige.zoom.us/j/873855655

Organisé par

Faculté d'économie et de management
Research Center for Statistics

Intervenants

Ioannis KOSMIDIS, University of Warwick, University College London, and Alan Turing Institute, UK

entrée libre

Classement

Catégorie: Séminaire

Plus d'infos

www.unige.ch/gsem/en/research/seminars/rcs/

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