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A critical assessment of bayesian modelling assumptions and their application to econometrics and environmental science


The aim of the methodological work is to broaden the scope of Bayesian modelling, so as to provided a more useful and realistic treatment for applied problems. We can categorize this work in three main groups: 1. Inference robustness: it consists in assessing the influence of modelling assumptions on the actual inference.
2. Modelling and inference through non-standard distributions: I introduce new classes of distributions that can be more adequate for modelling certain data sets (displaying e. g. skewness and/or fat tails). 3. Reference priors: it deals with finding a prior distribution which adequately captures a lack of prior information. I would like to find this reference prior for stochastic frontier models.
In terms of econometric applications, I would like to investigate theoretical and practical aspects of stochastic frontier models, as well as to treat financial data using nonstandard distributions as mentioned above. In environmental science, I am trying to develop a statistical model for estimating fish captures.


Warandelaan 2
5037 AB Tilburg

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