Showing papers by "Barry L. Nelson published in 2012"

Measurements of the T2K neutrino beam properties using the INGRID on-axis near detector

Abstract: Precise measurement of neutrino beam direction and intensity was achieved based on a new concept with modularized neutrino detectors. INGRID (Interactive Neutrino GRID) is an on-axis near detector for the T2K long baseline neutrino oscillation experiment. INGRID consists of 16 identical modules arranged in horizontal and vertical arrays around the beam center. The module has a sandwich structure of iron target plates and scintillator trackers. INGRID directly monitors the muon neutrino beam profile center and intensity using the number of observed neutrino events in each module. The neutrino beam direction is measured with accuracy better than 0.4 mrad from the measured profile center. The normalized event rate is measured with 4% precision.

The effects of common random numbers on stochastic kriging metamodels

Abstract: Ankenman et al. introduced stochastic kriging as a metamodeling tool for representing stochastic simulation response surfaces, and employed a very simple example to suggest that the use of Common Random Numbers (CRN) degrades the capability of stochastic kriging to predict the true response surface. In this article we undertake an in-depth analysis of the interaction between CRN and stochastic kriging by analyzing a richer collection of models; in particular, we consider stochastic kriging models with a linear trend term. We also perform an empirical study of the effect of CRN on stochastic kriging. We also consider the effect of CRN on metamodel parameter estimation and response-surface gradient estimation, as well as response-surface prediction. In brief, we confirm that CRN is detrimental to prediction, but show that it leads to better estimation of slope parameters and superior gradient estimation compared to independent simulation.

A quicker assessment of input uncertainty

Abstract: "Input uncertainty" refers the effect of driving a simulation with input distributions that are based on real-world data. At WSC 2012, Ankenman and Nelson presented a quick-and-easy experiment to assess the overall effect of input uncertainty on simulation output. When their method reveals that input uncertainty is substantial, then the natural follow-up questions are which input distributions contribute the most to input uncertainty, and from which input processes would it be most beneficial to collect more data? To answer these questions Ankenman and Nelson proposed a sequence of additional experiments that are in no sense "quick." In this paper we provide a follow-up analysis that requires no additional simulation experiments beyond the overall assessment, and yet provides more information than Ankenman and Nelson. Numerical illustrations are provided.

Stochastic kriging for conditional value-at-risk and its sensitivities

Abstract: Measuring risks in asset portfolios has been one of the central topics in the financial industry. Since the introduction of coherent risk measures, studies on risk measurement have flourished and measures beyond value-at-risk, such as expected shortfall, have been adopted by academics and practitioners. However, the complexity of financial products makes it very difficult and time consuming to perform the numerical tasks necessary to compute these risk measures. In this paper, we introduce a stochastic kriging metamodel-based method for efficient estimation of risks and their sensitivities. In particular, this method uses gradient estimators of assets in a portfolio and gives the best linear unbiased predictor of the risk sensitivities with minimum mean squared error. Numerical comparisons of the proposed method with two other stochastic kriging based approaches demonstrate the promising role that the proposed method can play in the estimation of financial risk.

Moving least squares regression for high dimensional simulation metamodeling

Abstract: Interpolation and smoothing methods form the basis of simulation metamodeling. In high dimensional metamodeling problems, larger numbers of design points are needed to build an accurate metamodel. This paper introduces a procedure to implement a smoothing method called Moving Least Squares regression in high dimensional metamodeling problems with a large number of design points. We test the procedure with two queueing examples: a multi-product M/G/1 queue and a multi-product Jackson network.