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Shrinking bias to benefit estimation and inference in statistical models

Location: D'Arcy Thompson Room (School of Computing Sciences, UEA)
Research Group: CMP Seminar
Date: 25 Jan 2013 (13:00-14:00)
Speaker: Dr. Ioannis Kosmidis
Organiser: Dr. Katharina Huber
Institution: Dept. of Statistical Science, University College London

Abstract

In this talk recent work on a unified computational and conceptual framework for reducing the bias in the estimation of statistical models is presented from a practitioners point of view. The talk will discuss several of the shortcomings of classical estimators (like the MLE) with demonstrations based on real and artificial data, for several well-used statistical models including Binomial and categorical responses models (for both nominal and ordinal responses) and Beta regression. The main focus will be on how those shortcomings can be overcome by reducing bias.

A generic algorithm of easy implementation for reducing the bias in any statistical model will also be presented along with specific purpose algorithms that take advantage of specific model structures.