Thomas J. Santner
Affiliations: | Ohio State University, Columbus, Columbus, OH |
Area:
StatisticsGoogle:
"Thomas Santner"Children
Sign in to add trainee| Dianne C. Bautista | grad student | 2009 | Ohio State |
| Fangfang Sun | grad student | 2012 | Ohio State |
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Publications
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| Davis CB, Hans CM, Santner TJ. (2020) Prediction of non-stationary response functions using a Bayesian composite Gaussian process Computational Statistics & Data Analysis. 107083 |
| Chen PA, Villarreal-Marroquín MG, Dean AM, et al. (2018) Sequential design of an injection molding process using a calibrated predictor Journal of Quality Technology. 50: 309-326 |
| Guo H, Santner TJ, Lerner AL, et al. (2017) Reducing uncertainty when using knee-specific finite element models by assessing the effect of input parameters. Journal of Orthopaedic Research : Official Publication of the Orthopaedic Research Society |
| Leatherman ER, Dean AM, Santner TJ. (2017) Designing combined physical and computer experiments to maximize prediction accuracy Computational Statistics & Data Analysis. 113: 346-362 |
| Leatherman ER, Santner TJ, Dean AM. (2017) Computer experiment designs for accurate prediction Statistics and Computing. 28: 739-751 |
| Svenson J, Santner T. (2016) Multiobjective optimization of expensive-to-evaluate deterministic computer simulator models Computational Statistics and Data Analysis. 94: 250-264 |
| Villarreal-Marroquín MG, Chen P, Mulyana R, et al. (2016) Multiobjective optimization of injection molding using a calibrated predictor based on physical and simulated data Polymer Engineering & Science. 57: 248-257 |
| Guo H, Santner TJ, Chen T, et al. (2015) A statistically-augmented computational platform for evaluating meniscal function. Journal of Biomechanics. 48: 1444-53 |
| Leatherman ER, Guo H, Gilbert SL, et al. (2014) Using a statistically calibrated biphasic finite element model of the human knee joint to identify robust designs for a meniscal substitute. Journal of Biomechanical Engineering. 136 |
| Svenson J, Santner T, Dean A, et al. (2014) Estimating sensitivity indices based on Gaussian process metamodels with compactly supported correlation functions Journal of Statistical Planning and Inference. 144: 160-172 |