Landmark-embedded Gaussian process with applications for functional data modeling

References

• Farmanesh, B., Pourhabib, A., Balasundaram, B. and Buchanan, A. (2020) A Bayesian framework for functional calibration of expensive computational models through non-isometric matching. IISE Transactions, 53(3), 352–364.
   Web of Science ®Google Scholar
• Gardner, J., Pleiss, G., Weinberger, K.Q., Bindel, D. and Wilson, A.G. (2018) Gpytorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration, in Advances in Neural Information Processing Systems, 31, 7576–7586.
   Google Scholar
• Gasser, T. and Kneip, A. (1995) Searching for structure in curve samples. Journal of the American Statistical Association, 90(432), 1179–1188.
   Web of Science ®Google Scholar
• Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A. and Rubin, D. (2013) Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC, New York, NY.
   Google Scholar
• Goh, J., Bingham, D., Holloway, J.P., Grosskopf, M.J., Kuranz, C.C. and Rutter, E. (2013) Prediction and computer model calibration using outputs from multifidelity simulators. Technometrics, 55(4), 501–512.
   Web of Science ®Google Scholar
• Gramacy, R.B., Bingham, D., Holloway, J.P., Grosskopf, M.J., Kuranz, C.C., Rutter, E., Trantham, M. and Drake, R.P. (2015) Calibrating a large computer experiment simulating radiative shock hydrodynamics. The Annals of Applied Statistics, 9(3), 1141–1168.
   Web of Science ®Google Scholar
• Guo, X., He, Y., Zhu, B., Yang, Y., Deng, K., Liu, R. and Ji, C. (2017) Bayesian uncertainty quantification for functional response, in Uncertainty Quantification and Model Calibration, InTech, London, UK.
   Google Scholar
• Han, D., Kim, B.S. and Chatterjee, A. (2009) DSP-driven self-tuning of RF circuits for process-induced performance variability. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 18(2), 305–314.
   Web of Science ®Google Scholar
• Herbrich, R., Lawrence, N. and Seeger, M. (2003) Fast sparse Gaussian process methods: The informative vector machine, in Advances in Neural Information Processing Systems, Volume 15, pp 609–616, MIT Press, Cambridge, MA.
   Google Scholar
• Higdon, D., Gattiker, J., Lawrence, E., Jackson, C., Tobis, M., Pratola, M., Habib, S., Heitmann, K. and Price, S. (2013) Computer model calibration using the ensemble kalman filter. Technometrics, 55(4), 488–500.
   Web of Science ®Google Scholar
• Ingrassia, S. and Morlini, I. (2005) Neural network modeling for small datasets. Technometrics, 47(3), 297–311.
   Web of Science ®Google Scholar
• Jin, J. and Shi, J. (1999) Feature-preserving data compression of stamping tonnage information using wavelets. Technometrics, 41(4), 327–339.
   Web of Science ®Google Scholar
• Kennedy, M.C. and O’Hagan, A. (2001) Bayesian calibration of computer models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63(3), 425–464.
   Web of Science ®Google Scholar
• Khosravi, A., Nahavandi, S., Creighton, D. and Atiya, A.F. (2011) Comprehensive review of neural network-based prediction intervals and new advances. IEEE Transactions on Neural Networks, 22(9), 1341–1356.
   PubMedGoogle Scholar
• Lee, B.S., Haran, M., Fuller, R.W., Pollard, D. and Keller, K. (2020) A fast particle-based approach for calibrating a 3-d model of the antarctic ice sheet. Annals of Applied Statistics, 14(2), 605–634.
   Web of Science ®Google Scholar
• Lei, Y., Zhang, Z. and Jin, J. (2010) Automatic tonnage monitoring for missing part detection in multi-operation forging processes. Journal of Manufacturing Science and Engineering, 132(5), 051010.1–051010.10.
   Web of Science ®Google Scholar
• Liu, Y., Dong, X. and Chen, P. (2012) Biological and chemical sensors based on graphene materials. Chemical Sociey Review, 41(6), 2283–2307.
   PubMed Web of Science ®Google Scholar
• Lu, Y., Subramani, K.S., Huang, H., Kupp, N., Huang, K. and Makris, Y. (2015) A comparative study of one-shot statistical calibration methods for analog/RF ICS, in 2015 IEEE International Test Conference (ITC), IEEE Press, Piscataway, NJ, pp. 1–10.
   Google Scholar
• Maity, A., Sui, X., Tarman, C.R., Pu, H., Chang, J., Zhou, G., Ren, R., Mao, S. and Chen, J. (2017) Pulse-driven capacitive lead ion detection with reduced graphene oxide field-effect transistor integrated with an analyzing device for rapid water quality monitoring. ACS Sensors, 2(11), 1653–1661.
   Web of Science ®Google Scholar
• Mosesova, S. (2007). Flexible Mixed-Effect Modeling of Functional Data, with Applications to Process Monitoring (Doctoral dissertation). UWSpace. http://hdl.handle.net/10012/3104
   Google Scholar
• Osborne, C. (1991) Statistical calibration: A review. International Statistical Review/Revue Internationale de Statistique, 59(3), 309–336.
   Web of Science ®Google Scholar
• Plumlee, M., Joseph, V.R. and Yang, H. (2016) Calibrating functional parameters in the ion channel models of cardiac cells. Journal of the American Statistical Association, 111(514), 500–509.
   Web of Science ®Google Scholar
• Ramsay, J.O., Gribble, P. and Kurtek, S. (2014) Analysis of juggling data: Landmark and continuous registration of juggling trajectories. Electronic Journal of Statistics, 8(2), 1835–1841.
   Google Scholar
• Ramsay, J.O. and Silverman, B.W. (2005) Functional Data Analysis, Springer New York, NY.
   Google Scholar
• Rasmussen, C.E. and Williams, C.K. (2006) Gaussian Processes for Machine Learning, Volume 1. MIT Press Cambridge, MA.
   Google Scholar
• Reza, S., Martin, N., Buchheit, T. and Tucker, J.D. (2020) Tolerance bound calculation for compact model calibration using functional data analysis, in 2020 4th IEEE Electron Devices Technology & Manufacturing Conference (EDTM), pp 1–4, IEEE Press, Piscataway, NJ.
   Google Scholar
• Santner, T.J., Williams, B.J., Notz, W. and Williams, B.J. (2003) The Design and Analysis of Computer Experiments, Volume 1. Springer, New York, NY.
   Google Scholar
• Thakur, B., Zhou, G., Chang, J., Pu, H., Jin, B., Sui, X., Yuan, X., Yang, C.-H., Magruder, M. and Chen, J. (2018) Rapid detection of single E. coli bacteria using a graphene-based field-effect transistor device. Biosensors and Bioelectronics, 110, 16–22.
   PubMed Web of Science ®Google Scholar
• Tsin, Y., Ramesh, V. and Kanade, T. (2001) Statistical calibration of CCD imaging process, in Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, Volume 1, IEEE Press, Piscataway, NJ, pp. 480–487.
   Google Scholar
• Wang, C., Pu, H., Sui, X., Zhou, S. and Chen, J. (2021) Hybrid modeling and sensitivity analysis on reduced graphene oxide field-effect transistor. IEEE Transactions on Nanotechnology, 20, 404–416.
   Web of Science ®Google Scholar
• Wang, S., Jin, Z., Huang, X., Peng, S., Zhang, D., and Shi, J. (2016) Abnormal Dirac point shift in graphene field-effect transistors. Materials Research Express, 3(9), 095602.
   Web of Science ®Google Scholar
• Zhang, C., Yan, H., Lee, S. and Shi, J. (2018) Weakly correlated profile monitoring based on sparse multi-channel functional principal component analysis. IISE Transactions, 50(10), 878–891.
   Web of Science ®Google Scholar
• Zhou, G., Chang, J., Cui, S., Pu, H., Wen, Z. and Chen, J. (2014) Real-time, selective detection of Pb2+ in water using a reduced graphene oxide/gold nanoparticle field-effect transistor device. ACS Applied Materials & Interfaces, 6(21), 19235–19241.
   Web of Science ®Google Scholar