Maximum likelihood estimation of structural models is regarded as computationally difficult. This impression is due to a focus on the Nested Fixed-Point approach. We present a direct optimization approach to the problem and show that it is significantly faster than the NFXP approach when applied to the canonical Zurcher bus repair model. The NFXP approach is inappropriate for estimating games s...

In this paper, we consider the robust portfolio selection problem for an insurer in the sense of maximizing the exponential utility of his wealth. This special robust investment problem, where underwriting results and a risk-free asset are considered, differs from ordinary robust portfolio selection problems. The insurer has the option of investing in a risk-free asset and multiple risky assets...

We consider the time horizon of a gambler in the optimization of horse race betting through the use of chance constrained programming. The optimization problem is formulated as a mixed integer nonlinear program for which global optimal solutions are found using optimization tools. A novel approach to estimating superfecta payouts is presented using maximum likelihood estimation. A computational...

We consider the problem of maximizing worst-case return a portfolio when manager can invest in stocks as well European options on those stocks, and stock returns are modeled using an uncertainty set approach. Specifically, knows range forecast for each factor driving budget limiting scaled deviations these factors from their nominal values. Our goal is to understand impact optimal allocation. p...

We study the empirical likelihood approach to construct confidence intervals for the optimal value and the optimality gap of a given solution, henceforth quantify the statistical uncertainty of sample average approximation, for optimization problems with expected value objectives and constraints where the underlying probability distributions are observed via limited data. This approach relies o...

 Ambiguity in the inputs of the models is typical especially in portfolio selection problem where the true distribution of random variables is usually unknown. Here we use robust optimization approach to address the ambiguity in conditional-value-at-risk minimization model. We obtain explicit models of the robust conditional-value-at-risk minimization for polyhedral and correlated polyhedral am...

This paper presents two memetic algorithms to solve multi-objective min-max problems, such as the ones that arise in evidence-based robust optimization. Indeed, the solutions that minimize the design budgets are robust under epistemic uncertainty if they maximize the belief in the realization of the value of the design budgets. Thus robust solutions are found by minimizing with respect to the d...

Optimization of reservoir parameters is an important issue in petroleum exploration and production. The Ant Colony Optimization(ACO) is a recent approach to solve discrete and continuous optimization problems. In this paper, the Ant Colony Optimization is usedas an intelligent tool to estimate reservoir rock properties. The methodology is illustrated by using a case study on shear wave velocity...