J. Staum

Publications76
h-index21
Citations1,883
Highly Influential Citations227
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Stochastic kriging for simulation metamodeling
We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Our goal is to provide flexible,…
Stochastic kriging for simulation metamodeling
We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Our goal is to provide flexible,…
Shapley Effects for Global Sensitivity Analysis: Theory and Computation
TLDR
Owen proposed an alternative sensitivity measure, based on the concept of the Shapley value in game theory, and showed it always sums to the correct total variance if inputs are independent, and it is analyzed, which is called Owen's measure, in the case of dependent inputs.
Fundamental Theorems of Asset Pricing for Good Deal Bounds
  • J. Staum
  • Mathematics, Economics
  • 1 April 2004
We prove fundamental theorems of asset pricing for good deal bounds in incomplete markets. These theorems relate arbitrage‐freedom and uniqueness of prices for over‐the‐counter derivatives to…
Better simulation metamodeling: The why, what, and how of stochastic kriging
  • J. Staum
  • Computer Science
    Proceedings of the Winter Simulation Conference…
  • 13 December 2009
TLDR
An exposition of how to choose parameters in stochastic kriging and how to build a metamodel with it given simulation output is provided, and the future research directions to enhance StochasticKriging are discussed.
Efficient Nested Simulation for Estimating the Variance of a Conditional Expectation
TLDR
It is shown that an ANOVA-like estimator of the variance of the conditional expectation is unbiased under mild conditions, and the optimal number of inner-level samples to minimize this estimator's variance given a fixed computational budget.
Conditioning on One-Step Survival for Barrier Option Simulations
TLDR
Variance reduction techniques that take advantage of the special structure of barrier options, and are appropriate for general simulation problems with similar structure, are developed.
Empirical likelihood for value-at-risk and expected shortfall
When estimating risk measures, whether from historical data or by Monte Carlo simulation, it is helpful to have confidence intervals that provide information about statistical uncertainty. We provide…
Performance Persistence in the Alternative Investment Industry
We construct an improved measure of skill among commodity trading advisors (CTAs) and hedge fund managers. The theoretical issues surrounding the possibility of internal leverage receive particular…
Gaussian Markov Random Fields for Discrete Optimization via Simulation: Framework and Algorithms
TLDR
It is shown that, for a discrete problem, GMRFs, a type ofGaussian process defined on a graph, provides better inference on the remaining optimality gap than the typical choice of continuous Gaussian process and thereby enables the algorithm to search efficiently and stop correctly when the remaining Optimality gap is below a predefined threshold.
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