Optimization of distortion riskmetrics with distributional uncertainty has wide applications in
finance and operations research. Distortion riskmetrics include many commonly applied risk …

In this paper, we study the risk-sharing problem among multiple agents using lambda value
at risk (Λ VaR) as their preferences via the tool of inf-convolution, where Λ VaR is an …

In this paper, we study the risk sharing problem among multiple agents using Lambda Value-
at-Risk as their preference functional, under heterogeneous beliefs, where beliefs are …

In this paper, we investigate the robust models for $\Lambda $-quantiles with partial
information regarding the loss distribution, where $\Lambda $-quantiles extend the classical …

In this paper, we study the optimal reinsurance design from the perspective of an insurer
with multiple lines of business, where the reinsurance is purchased by the insurer for each …

In this paper, we compute the tightest possible bounds on the probability that the optimal
value of a combinatorial optimization problem in maximization form with a random objective …

We study a methodology to tackle the NASA Langley Uncertainty Quantification Challenge,
a model calibration problem under both aleatory and epistemic uncertainties. Our …

A joint mix (JM) is a random vector with a constant component-wise sum. The dependence
structure of a joint mix minimizes some common objectives, such as the variance of the …

Aggregation sets, which represent model uncertainty due to unknown dependence, are an
important object in the study of robust risk aggregation. In this paper, we investigate ordering …

Classic optimal transport theory is built on minimizing the expected cost between two given
distributions. We propose the framework of distorted optimal transport by minimizing a …