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In this paper, we focus on a linear optimization problem with uncertainties, having expectations in the objective and in the set of constraints. We present a modular framework to obtain an approximate solution to the problem that is distributionally robust, and more flexible than the standard technique of using linear rules. Our framework begins by firstly(More)
We propose a new approach to portfolio optimization by separating asset return distributions into positive and negative half-spaces. The approach minimizes a so-called Partitioned Value-at-Risk (PVaR) measure by using half-space statistical information. Using simulated and real data, the PVaR approach generates better risk-return tradeoffs in the optimal(More)
Active postmarketing drug surveillance is important for consumer safety. However, existing methods have limitations that prevent their direct use for active drug surveillance. One important consideration that has been absent thus far is the modeling of multiple adverse events and their interactions. In this paper, we propose a method to monitor the effect(More)
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