Forthcoming Issue
The following papers are expected to appear in
Volume 28, Number 2, April 2018
Computer Experiments and Uncertainty Quantification |
1. SS-2016-0217 |
doi:10.5705/ss.202016.0217 |
Computer experiments: prediction accuracy, sample size and model complexity revisited
Or Harari, Derek Bingham, Angela Dean and Dave Higdon |
2. SS-2016-0130 |
doi:10.5705/ss.202016.0130 |
Sensitivity analysis and emulation for functional data using bayesian adaptive splines
Devin Francom, Bruno Sanso, Ana Kupresanin and Gardar Johannesson |
3. SS-2016-0035 |
doi:10.5705/ss.202016.0035 |
Sensitivity analysis using permutations
Shifeng Xiong, Xu He, Yuanzhen He and Weiyan Mu |
4. SS-2016-0165 |
doi:10.5705/ss.202016.0165 |
A sequential maximum projection design framework for computer experiments with inert factors
Shan Ba, William R. Myers and Dianpeng Wang |
5. SS-2016-0255 |
doi:10.5705/ss.202016.0255 |
Single nugget kriging
Minyong R. Lee and Art B. Owen |
6. SS-2015-0404 |
doi:10.5705/ss.202015.0404 |
Orthogonal Gaussian process models
Matthew Plumlee and V. Roshan Joseph |
7. SS-2015-0367 |
doi:10.5705/ss.202015.0367 |
Uncertainty quantification with Ł\-stable-process models
Rui Tuo |
8. SS-2015-0249 |
doi:10.5705/ss.202015.0249 |
Gaussian process modeling with boundary information
Matthias Hwai Yong Tan |
9. SS-2015-0344 |
doi:10.5705/ss.202015.0344 |
Nonparametric functional calibration of computer models
D. Andrew Brown and Sez Atamturktur |
10. SS-2016-0160 |
doi:10.5705/ss.202016.0160 |
Sequential design of experiments for estimating quantiles of black-box functions
T. Labopin-Richard and V. Picheny |
11. SS-2016-0163 |
doi:10.5705/ss.202016.0163 |
Sequential Pareto minimization of physical systems using calibrated computer simulators
Po-Hsu Allen Chen, Thomas J. Santner and Angela M. Dean |
12. SS-2016-0138 |
doi:10.5705/ss.202016.0138 |
Exploiting variance reduction potential in local Gaussian process search
Chih-Li Sung, Robert B. Gramacy and Benjamin Haaland |
13. SS-2016-0403 |
doi:10.5705/ss.202016.0403 |
Bayesian calibration of multistate stochastic simulators
Mathew T. Pratola and Oksana Chkrebtii |
14. SS-2016-0162 |
doi:10.5705/ss.202016.0162 |
Statistical-physical estimation of pollution emission
Youngdeok Hwang, Emre Barut and Kyongmin Yeo |
15. SS-2017-0138 |
doi:10.5705/ss.202017.0138 |
Surrogate-assisted tuning for computer experiments with qualitative and quantitative parameters
Jiahong K. Chen, Ray-Bing Chen, Akihiro Fujii, Reiji Suda and Weichung Wang |
16. SS-2016-0209 |
doi:10.5705/ss.202016.0209 |
Prediction based on the Kennedy-O'Hagan calibration model: asymptotic consistency and other properties
Rui Tuo and C. F. Je Wu |
17. SS-2016-0151 |
doi:10.5705/ss.202016.0151 |
Controlling correlations in sliced Latin hypercube designs
Jiajie Chen and Peter Qian |
18. SS-2017-0091 |
doi:10.5705/ss.202017.0091 |
Generalized sparse precision matrix selection for fitting multivariate
Sam Davanloo Tajbakhsh, Necdet Serhat Aybat and Enrique del Castillo |
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General |
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19. SS-2016-0041 |
doi:10.5705/ss.202016.0041 |
High-dimensional Gaussian copula regression: adaptive estimation and statistical inference
T. Tony Cai and Linjun Zhang |
20. SS-2016-0434 |
doi:10.5705/ss.202016.0434 |
Multi-asset empirical martingale price estimators for financial derivatives
Shih-Feng Huang and Guan-Chih Ciou |
21. SS-2016-0080 |
doi:10.5705/ss.202016.0080 |
Flexible imension reduction in regression
Tao Wang and Lixing Zhu |
22. SS-2016-0441 |
doi:10.5705/ss.202016.0441 |
Fully efficient robust estimation, outlier detection and variable selection via penalized regression
Dehan Kong, Howard D. Bondell and Yichao Wu |
23. SS-2016-0167 |
doi:10.5705/ss.202016.0167 |
Scalable Bayesian variable selection using nonlocal prior densities in ultrahigh-dimensional settings
Minsuk Shin, Anirban Bhattacharya and Valen E. Johnson |
24. SS-2016-0378 |
doi:10.5705/ss.202016.0378 |
Estimating standard errors for importance sampling estimators with multiple Markov chains
Vivekananda Roy, Aixin Tan and James M. Flegal |