Merrill W. Liechty

TL;DR: This work proposes a method for optimal portfolio selection using a Bayesian decision theoretic framework that addresses two major shortcomings of the traditional Markowitz approach: the ability to handle higher moments and parameter uncertainty, and employs the skew normal distribution.

TL;DR: In this article, a Bayesian decision theoretic framework was proposed for optimal portfolio selection using a skew normal distribution, which has many attractive features for modeling multivariate returns. But, it is important to incorporate higher order moments in portfolio selection, which leads to higher expected utility than the traditional Markowitz approach.

TL;DR: This work proposes prior probability models for variance-covariance matrices that allow a researcher to represent substantive prior information about the strength of correlations among a set of variables, and discusses appropriate posterior simulation schemes to implement posterior inference in the proposed models.

TL;DR: In this article, a full factorial experimental design considering all the factors described in the literature, and then analysing (by using analysis of variance and linear regression models), thousands of theoretical and extreme structures of supply networks, thus allowing the analysis of the influence of each factor on the overall network resilience.

TL;DR: An administrative and market-based optimization method for solving a problem of regional water resources allocation by considering a hierarchical structure under multiple uncertainties using a multi-objective bi-level programming model based on the water right distribution in a river basin is presented.