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Modeling and optimization of risk
This paper surveys the most recent advances in the context of decision making under
uncertainty, with an emphasis on the modeling of risk-averse preferences using the …
uncertainty, with an emphasis on the modeling of risk-averse preferences using the …
Uncertainty quantification in machining deformation based on Bayesian network
Uncertainty quantification in the analysis of machining systems is of great importance for
continuously improving product quality, reliability, and efficiency of manufacturing …
continuously improving product quality, reliability, and efficiency of manufacturing …
Diversification-consistent data envelopment analysis with general deviation measures
We propose new efficiency tests which are based on traditional DEA models and take into
account portfolio diversification. The goal is to identify the investment opportunities that …
account portfolio diversification. The goal is to identify the investment opportunities that …
[HTML][HTML] Parametric measures of variability induced by risk measures
We present a general framework for a comparative theory of variability measures, with a
particular focus on the recently introduced one-parameter families of inter-Expected Shortfall …
particular focus on the recently introduced one-parameter families of inter-Expected Shortfall …
How risky is the optimal portfolio which maximizes the Sharpe ratio?
In this paper, we investigate the properties of the optimal portfolio in the sense of maximizing
the Sharpe ratio (SR) and develop a procedure for the calculation of the risk of this portfolio …
the Sharpe ratio (SR) and develop a procedure for the calculation of the risk of this portfolio …
Statistical decision problems
In an abstract form, statistical decision making is an optimization problem that uses available
statistical data as an input and optimizes an objective function of interest with respect to …
statistical data as an input and optimizes an objective function of interest with respect to …
[HTML][HTML] Tight tail probability bounds for distribution-free decision making
Chebyshev's inequality provides an upper bound on the tail probability of a random variable
based on its mean and variance. While tight, the inequality has been criticized for only being …
based on its mean and variance. While tight, the inequality has been criticized for only being …
Stochastic optimization of sensor placement for diver detection
A comprehensive framework for diver detection by a hydrophone network in an urban harbor
is presented. It includes a signal processing algorithm and a diver detection test and …
is presented. It includes a signal processing algorithm and a diver detection test and …
Sensitivity analysis in applications with deviation, risk, regret, and error measures
The envelope formula is obtained for optimization problems with positively homogeneous
convex functionals defined on a space of random variables. Those problems include linear …
convex functionals defined on a space of random variables. Those problems include linear …
Calculating exceedance probabilities using a distributionally robust method
Calculation of exceedance probabilities or the inverse problem of finding the level
corresponding to a given exceedance probability occurs in many practical applications. For …
corresponding to a given exceedance probability occurs in many practical applications. For …