Browsing by Subject "Model Uncertainty"

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    Bayesian Model Averaging in the M-Open Framework
    (Bayesian Theory and Applications, 2013) Clydec, Merlise; Iversen, Edwin S
    This chapter presents a model averaging approach in the M-open setting using sample re-use methods to approximate the predictive distribution of future observations. It first reviews the standard M-closed Bayesian Model Averaging approach and decision-theoretic methods for producing inferences and decisions. It then reviews model selection from the M-complete and M-open perspectives, before formulating a Bayesian solution to model averaging in the M-open perspective. It constructs optimal weights for MOMA:M-open Model Averaging using a decision-theoretic framework, where models are treated as part of the ‘action space’ rather than unknown states of nature. Using ‘incompatible’ retrospective and prospective models for data from a case-control study, the chapter demonstrates that MOMA gives better predictive accuracy than the proxy models. It concludes with open questions and future directions.
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    Essays on Online Decisions, Model Uncertainty and Learning
    (2017) Nguyen, Van Vinh

    This dissertation examines optimal solutions in complex decision problems with one or more of the following components: online decisions, model uncertainty and learning. The first model studies the problem of online selection of a monotone subsequence and provides distributional properties of the optimal objective function. The second model studies the robust optimization approach to the decision problem of an auction bidder who has imperfect information about rivals' bids and wants to maximize her worst-case payoff. The third model analyzes the performance of a myopic Bayesian policy and one of its variants in the dynamic pricing problem of a monopolistic insurer who sells a business interruption insurance product over a planning horizon.