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Jerome Sacks
Research Triangle Park
63Publications
26H-index
7,922Citations
Publications 63
Newest
#1Daniel KieferH-Index: 1
#2Jerome SacksH-Index: 26
Last.Donald Ylvisaker (UCLA: University of California, Los Angeles)H-Index: 16
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A sidelining of statistical evidence in civil rights cases has been 30 years in the making. By Daniel Kiefer, Jerome Sacks and Donald Ylvisaker
1 CitationsSource
#1Hao ChenH-Index: 2
#2Jason L. LoeppkyH-Index: 8
Last.William J. WelchH-Index: 26
view all 4 authors...
Statistical methods based on a regression model plus a zero-mean Gaussian process (GP) have been widely used for predicting the output of a deterministic computer code. There are many suggestions in the literature for how to choose the regression component and how to model the correlation structure of the GP. This article argues that comprehensive, evidence-based assessment strategies are needed when comparing such modeling options. Otherwise, one is easily misled. Applying the strategies to sev...
12 CitationsSource
Two different approaches to the prediction problem are compared employing a realistic example---combustion of natural gas---with 102 uncertain parameters and 76 quantities of interests. One approach, termed bound-to-bound data collaboration (abbreviated to B2B), deploys semidefinite programming algorithms where the initial bounds on unknowns are combined with initial bounds of experimental data to produce new uncertainty bounds for the unknowns that are consistent with the data and, finally, det...
10 CitationsSource
#2Jerome SacksH-Index: 26
Last.William J. WelchH-Index: 26
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This is an exchange between Jerome Sacks and Donald Ylvisaker covering their career paths along with some related history and philosophy of Statistics.
2 CitationsSource
#2Jason L. LoeppkyH-Index: 8
Last.Jerome SacksH-Index: 26
view all 3 authors...
#1Jason L. LoeppkyH-Index: 8
#2Jerome SacksH-Index: 26
Last.William J. WelchH-Index: 26
view all 3 authors...
We provide reasons and evidence supporting the informal rule that the number of runs for an effective initial computer experiment should be about 10 times the input dimension. Our arguments quantify two key characteristics of computer codes that affect the sample size required for a desired level of accuracy when approximating the code via a Gaussian process (GP). The first characteristic is the total sensitivity of a code output variable to all input variables; the second corresponds to the way...
290 CitationsSource
#1Baohong WanH-Index: 1
#2Nagui M. RouphailH-Index: 33
Last.Jerome SacksH-Index: 26
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Effective and feasible procedures for validating microscopic, stochastic traffic simulation models are in short supply. Exercising such microsimulators many times may lead to the occurrence of traffic gridlock (or simulation failures) on some or all replications. Whereas lack of failures does not ensure validity of the simulator for predicting performance, the occurrence of failures can provide clues for identifying deficiencies of the simulation model and invite strategies for model improvement...
Source
#1Baohong WanH-Index: 1
#2Nagui M. Rouphail (NCSU: North Carolina State University)H-Index: 33
Last.Jerome Sacks (RTP: Research Triangle Park)H-Index: 26
view all 3 authors...
Effective and feasible procedures for validating microscopic, stochastic traffic simulation models are in short supply. Exercising such microsimulators many times may lead to the occurrence of traffic gridlock (or simulation failures) on some or all replications. Whereas lack of failures does not ensure validity of the simulator for predicting performance, the occurrence of failures can provide clues for identifying deficiencies of the simulation model and invite strategies for model improvement...
1 CitationsSource
#1Rui Miguel Batista Paulo (UoB: University of Bristol)H-Index: 1
#2Jiong Lin (North Carolina Department of Transportation)H-Index: 1
Last.Jerome Sacks (RTP: Research Triangle Park)H-Index: 26
view all 4 authors...
Calibration and validation of traffic models are processes that depend on field data that are often limited but are essential for determination of inputs to the model and assessment of its reliability. Quantification and systematization of the calibration and validation process expose statistical issues inherent in the use of such data. Formalization of the calibration and validation process naturally leads to the use of Bayesian methodology for assessment of uncertainties in model predictions t...
2 CitationsSource
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