• Publications
  • Influence
Stochastic kriging for simulation metamodeling
TLDR
We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Expand
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  • 63
  • PDF
Stochastic kriging for simulation metamodeling
TLDR
We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Expand
  • 162
  • 48
Shapley Effects for Global Sensitivity Analysis: Theory and Computation
TLDR
Variance-based global sensitivity analysis decomposes the variance of the output of a computer model, resulting from uncertainty about the model's inputs, into variance components associated with each input's contribution. Expand
  • 56
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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
Stochastic kriging is a methodology recently developed for metamodeling stochastic simulation. Expand
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  • 6
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Fundamental Theorems of Asset Pricing for Good Deal Bounds
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 toExpand
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Conditioning on One-Step Survival for Barrier Option Simulations
TLDR
This paper develops variance reduction techniques that take advantage of the special structure of barrier options, and are appropriate for general simulation problems with similar structure. Expand
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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 particularExpand
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  • 5
Efficient Nested Simulation for Estimating the Variance of a Conditional Expectation
TLDR
An ANOVA-like estimator of the variance of the conditional expectation is unbiased under mild conditions, and we discuss the optimal number of inner-level samples to minimize this estimator's variance given a fixed computational budget. Expand
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Simulation of Coherent Risk Measures Based on Generalized Scenarios
TLDR
We propose procedures to form fixed-width, simulation-based confidence intervals for the maximum of several expectations, explore their correctness and computational efficiency, and illustrate them on risk-management problems. Expand
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A confidence interval for tail conditional expectation via two-level simulation
TLDR
We develop and evaluate a two-level simulation procedure that produces a confidence interval for tail conditional expectation, otherwise known as conditional tail expectation. Expand
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