Model selection: Consistency and robustness properties of the Schwarz Information Criterion for generalized M-estimation

This thesis main focus are the robustness properties of the Schwarz Information Criterion (SIC) based on sample objective functions defining (Bias) robust M-estimators. The Bayesian underpinnings of such a criterion are established by extending Schwarz's original framework to densities not belonging to the exponential family. A definition of qualitative robustness appropriate for model selection is provided and it is shown that the crucial restriction needed to achieve robustness is the uniform boundedness of the objective function defining Bias robust M-estimators. In this process, the asymptotic performance of the SIC for generalized M-estimators is also studied. The finite sample behavior of the SIC for different types of M-estimators is analyzed by means of Monte Carlo experiments.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Economics

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Machado, Jose Antonio Ferreira

Contributors dc:contributor

• Koenker, Roger W.

• Statistics
• Economics, Theory

Statement dc:rights

• Copyright 1989 Machado, Jose Antonio Ferreira

Language dc:language

eng

Identifier

AAI8924813
(UMI)AAI8924813

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/21433