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Robust Bayesian Analysis

thesis
posted on 14.05.2018, 02:29 by ZHICHAO LIU
This thesis develops two robust Bayesian inferential methods to handle model misspecification due to the presence of outliers in the data. These new methods are designed to produce robust Bayesian inference based on simple parametric models which may be misspecified. Utilizing robust information from the data, these Bayesian methods are able to produce posterior inference about the fundamental relationship between variables that is robust with respect to outliers.

History

Campus location

Australia

Principal supervisor

Catherine Scipione Forbes

Additional supervisor 1

Heather Anderson

Year of Award

2018

Department, School or Centre

Econometrics and Business Statistics

Course

Doctor of Philosophy

Degree Type

DOCTORATE

Faculty

Faculty of Business and Economics

Exports

Exports