Sequential adaptive design for jump regression estimation

References

• Arias-Castro, E., Candes, E.J. and Davenport, M.A. (2013) On the fundamental limits of adaptive sensing. IEEE Transactions on Information Theory, 59(1), 472–481.
   Web of Science ®Google Scholar
• Bull, A.D. (2013) Spatially-adaptive sensing in nonparametric regression. The Annals of Statistics, 41(1), 41–62.
   Web of Science ®Google Scholar
• Carpena-Núñez, J., Nikolaev, P., Susner, M., Rao, R., Gorsse, S., Park, C. and Maruyama, B. (2020) Mapping carbon nanotube catalyst phase transitions using jump regression. Unpublished manuscript.
   Google Scholar
• Chaudhuri, P. and Mykland, P.A. (1993) Nonlinear experiments: Optimal design and inference based on likelihood. Journal of the American Statistical Association, 88(422), 538–546.
   Web of Science ®Google Scholar
• Chernoff, H. (1972) Sequential Analysis and Optimal Design. Society for Industrial and Applied Mathematics, Philadelphia PA.
   Google Scholar
• Cohn, D.A., Ghahramani, Z. and Jordan, M.I. (1996) Active learning with statistical models. Journal of Artificial Intelligence Research, 4(1), 129–145.
   Web of Science ®Google Scholar
• Cortes, C., DeSalvo, G., Gentile, C., Mohri, M. and Zhang, N. (2019) Region-based active learning, in Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, Volume 89, pp. 2801–2809, PMLR, Okinawa, Japan.
   Google Scholar
• Dror, H.A. and Steinberg, D.M. (2008) Sequential experimental designs for generalized linear models. Journal of the American Statistical Association, 103(481), 288–298.
   Web of Science ®Google Scholar
• Goetz, J., Tewari, A. and Zimmerman, P. (2018) Active learning for non-parametric regression using purely random trees, in Advances in Neural Information Processing Systems, Proceedings of 32nd Conference on Neural Information Processing Systems, Volume 31, pp. 2542–2551, MIT Press, Montréal, Canada.
   Google Scholar
• Gramacy, R.B. and Lee, H.K. (2009) Adaptive design and analysis of supercomputer experiments. Technometrics, 51(2), 130–145.
   Web of Science ®Google Scholar
• Hoang, T.N., Low, B.K.H., Jaillet, P. and Kankanhalli, M. (2014) Nonmyopic ϵ-Bayes-optimal active learning of Gaussian processes, in Proceedings of the 31st International Conference on Machine Learning, Volume 32, pp. 739–747, PMLR, Beijing, China.
   Google Scholar
• Kim, H.-M., Mallick, B.K. and Holmes, C.C. (2005) Analyzing nonstationary spatial data using piecewise Gaussian processes. Journal of the American Statistical Association, 100(470), 653–668.
   Web of Science ®Google Scholar
• Krause, A., Singh, A. and Guestrin, C. (2008) Near-optimal sensor placements in Gaussian processes: Theory, efficient algorithms and empirical studies. Journal of Machine Learning Research, 9(1), 235–284.
   Google Scholar
• Mack, Y. and Rosenblatt, M. (1979) Multivariate k-nearest neighbor density estimates. Journal of Multivariate Analysis, 9(1), 1–15.
   Web of Science ®Google Scholar
• Malloy, M.L. and Nowak, R.D. (2014) Near-optimal adaptive compressed sensing. IEEE Transactions on Information Theory, 60(7), 4001–4012.
   Web of Science ®Google Scholar
• Nikolaev, P., Hooper, D., Perea-Lopez, N., Terrones, M. and Maruyama, B. (2014) Discovery of wall-selective carbon nanotube growth conditions via automated experimentation. ACS Nano, 8(10), 10214–10222.
   Web of Science ®Google Scholar
• Nikolaev, P., Hooper, D., Webber, F., Rao, R., Decker, K., Krein, M., Poleski, J., Barto, R. and Maruyama, B. (2016) Autonomy in materials research: A case study in carbon nanotube growth. npj Computational Materials, 2(16031), 1–6.
   Google Scholar
• Paisley, J., Liao, X. and Carin, L. (2010) Active learning and basis selection for kernel-based linear models: A Bayesian perspective. IEEE Transactions on Signal Processing, 58(5), 2686–2700.
   Web of Science ®Google Scholar
• Park, C. and Ding, Y. (2019) Automating material image analysis for material discovery. MRS Communications, 9(2), 545–555.
   Web of Science ®Google Scholar
• Park, C. and Ding, Y. (2021) Data Science for Nano Image Analysis. Springer, Switzerland.
   Google Scholar
• Pope, C.A., Gosling, J.P., Barber, S., Johnson, J.S., Yamaguchi, T., Feingold, G. and Blackwell, P.G. (2021) Gaussian process modeling of heterogeneity and discontinuities using Voronoi tessellations. Technometrics, 63(1), 53–63.
   Web of Science ®Google Scholar
• Qiu, P. (1998) Discontinuous regression surfaces fitting. The Annals of Statistics, 26(6), 2218–2245.
   Web of Science ®Google Scholar
• Qiu, P. (2004) The local piecewisely linear kernel smoothing procedure for fitting jump regression surfaces. Technometrics, 46(1), 87–98.
   Web of Science ®Google Scholar
• Qiu, P. (2005) Image Processing and Jump Regression Analysis. John Wiley & Sons, Hoboken, NJ.
   Google Scholar
• Qiu, P. (2009) Jump-preserving surface reconstruction from noisy data. Annals of the Institute of Statistical Mathematics, 61(3), 715–751.
   Web of Science ®Google Scholar
• Qiu, P. and Yandell, B. (1997) Jump detection in regression surfaces. Journal of Computational and Graphical Statistics, 6(3), 332–354.
   Web of Science ®Google Scholar
• Ranjan, P., Bingham, D. and Michailidis, G. (2008) Sequential experiment design for contour estimation from complex computer codes. Technometrics, 50(4), 527–541.
   Web of Science ®Google Scholar
• Rao, R., Liptak, D., Cherukuri, T., Yakobson, B.I. and Maruyama, B. (2012) In situ evidence for chirality-dependent growth rates of individual carbon nanotubes. Nature Materials, 11(3), 213–216.
   Web of Science ®Google Scholar
• Sacks, J., Welch, W.J., Mitchell, T.J. and Wynn, H.P. (1989) Design and analysis of computer experiments. Statistical Science, 4(4), 409–423.
   Google Scholar
• Singh, A., Krause, A., Guestrin, C. and Kaiser, W.J. (2009) Efficient informative sensing using multiple robots. Journal of Artificial Intelligence Research, 34(1), 707–755.
   Google Scholar
• Stach, E., DeCost, B., Kusne, A.G., Hattrick-Simpers, J., Brown, K.A., Reyes, K.G., Schrier, J., Billinge, S., Buonassisi, T., Foster, I., Gomes, C.P., Gregoire, J.M., Mehta, A., Montoya, J., Olivetti, E., Park, C., Rotenberg, E., Saikin, S.K., Smullin, S., Stanev, V. and Maruyama, B. (2021) Autonomous experimentation systems for materials development: A community perspective. Matter, 4(9), 2702–2726.
   Google Scholar
• Stevens, A., Kovarik, L., Abellan, P., Yuan, X., Carin, L. and Browning, N.D. (2015) Applying compressive sensing to TEM video: A substantial frame rate increase on any camera. Advanced Structural and Chemical Imaging, 1(10), 1–20.
   Google Scholar
• Taddy, M.A., Gramacy, R.B. and Polson, N.G. (2011) Dynamic trees for learning and design. Journal of the American Statistical Association, 106(493), 109–123.
   Web of Science ®Google Scholar
• Wasserman, L. (2006) All of Nonparametric Statistics. Springer-Verlag, New York, NY.
   Google Scholar
• Zhao, Z. and Yao, W. (2012) Sequential design for nonparametric inference. Canadian Journal of Statistics, 40(2), 362–377.
   Web of Science ®Google Scholar
• Zhu, Z. and Stein, M.L. (2006) Spatial sampling design for prediction with estimated parameters. Journal of Agricultural, Biological, and Environmental Statistics, 11(1), 24–44.
   Web of Science ®Google Scholar
• Zimmerman, D.L. (2006) Optimal network design for spatial prediction, covariance parameter estimation, and empirical prediction. Environmetrics, 17(6), 635–652.
   Web of Science ®Google Scholar