References
- M.L. Benson, M.N. Tran, and M.R. Hill, Phase 2b weld residual stress round robin: Mockup design and comparisons of measurement and simulation results, Proceedings of the ASME Pressure Vessels and Piping Conference. 2015.
- A. Bhattacharya, On a measure of divergence between two statistical populations defined by their probability distributions, Bullet. Calcutta Math. Soc. 35 (1943), pp. 99–109.
- R. Bousseljot, D. Kreiseler, and A. Schnabel, Nutzung der EKG-signaldatenbank CAR-DIODAT der PTB uber das internet, Biomedizinische Technik 40 (1995), pp. S317–S318.
- A.C. Davison and D.V. Hinkley, Bootsrap Methods and Their Application, Cambridge University Press, Cambridge, 1997.
- A.C. Davison, D.V. Hinkley, and G.A. Young, Recent developments in bootstrap methodology, Stat. Sci. 18 (2003), pp. 141–157. doi: https://doi.org/10.1214/ss/1063994969
- F. Ferraty and P. Vieu, Nonparametric Functional Data Analysis: Theory and Practice, Springer-Verlag, New York, 2006.
- A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C. Peng, and H.E. Stanley, Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals, Circulation 101 (2000), pp. e215–e220. Available at http://www.physionet.org.
- M. Grasso, A. Menafoglio, B.M. Colosimo, and P. Secchi, Using curve-registration information for profile monitoring, J. Quality Tech. 48 (2016), pp. 99–127. doi: https://doi.org/10.1080/00224065.2016.11918154
- G.J. Hahn and W.Q. Meeker, Statistical Intervals: A Guide for Practitioners, John Wiley & Sons, Hoboken, 2011.
- A. Kneip and J.O. Ramsay, Combining registration and fitting for functional models, J. Am. Stat. Assoc. 103 (2008). doi: https://doi.org/10.1198/016214508000000517
- K. Krishnamoorthy and T. Matthew, Statistical Tolerance Regions: Theory, Applications, and Computation, Wiley, New York, 2009.
- K. Krishnamoorthy and S. Mondal, Improved tolerance factors for multivariate normal distributions, Commun. Stat. Simulation Comput. 35 (2006), pp. 461–478. doi: https://doi.org/10.1080/03610910600591883
- S. Kurtek, A geometric approach to pairwise Bayesian alignment of functional data using importance sampling, Electron. J. Stat. 11 (2017), pp. 502–531. doi: https://doi.org/10.1214/17-EJS1243
- S. Kurtek and K. Bharath, Bayesian sensitivity analysis with Fisher–Rao metric, Biometrika 102 (2015), pp. 601–616. doi: https://doi.org/10.1093/biomet/asv026
- S. Kurtek, A. Srivastava, and W. Wu, Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment, Proceedings of Neural Information Processing Systems (NIPS). 2011.
- S. Kurtek, W. Wu, G.E. Christensen, and A. Srivastava, Segmentation, alignment and statistical analysis of biosignals with application to disease classification, J. Appl. Stat. 40 (2013), pp. 1270–1288. doi: https://doi.org/10.1080/02664763.2013.785492
- S. Lahiri, D. Robinson, and E. Klassen, Precise matching of PL curves in Rn in the Square Root Velocity framework, Geometry Imag. Comput. 2 (2015), pp. 133–186. doi: https://doi.org/10.4310/GIC.2015.v2.n3.a1
- S. Lee and S. Jung, Combined Analysis of Amplitude and Phase Variations in Functional Data, arXiv:1603.01775 [stat.ME] (2017), pp. 1–21. Available at https://arxiv.org/abs/1603.01775.
- J.R. Lewis, D. Brooks, and M.L. Benson, Methods for Uncertainty Quantification and Comparison of Weld Residual Stress Measurements and Predictions, Proceedings of the ASME Pressure Vessels and Piping Conference.2017.
- Y. Lu, R. Herbei, and S. Kurtek, Bayesian registration of functions with a Gaussian process prior, J. Comput. Graph. Stat. 26 (2017), pp. 894–904. doi: https://doi.org/10.1080/10618600.2017.1336444
- A.H. Mahmoudi, S. Hossain, C.E. Truman, D.J. Smith, and M.J. Pavier, A new procedure to measure near yield residual stresses using the deep hole drilling technique, Exp. Mech. 49 (2008), pp. 595–604. doi: https://doi.org/10.1007/s11340-008-9164-y
- J. Marron, J. Ramsay, L. Sangalli, and A. Srivastava, Functional data analysis of amplitude and phase variation, Stat. Sci. 30 (2015), pp. 468–484. doi: https://doi.org/10.1214/15-STS524
- M.B. Prime, R.J. Sebring, J.M. Edwards, D.J. Hughes, and P.J. Webster, Laser surface-contouring and spline data-smoothing for residual-stress measurement, Exp. Mech. 44 (2004), pp. 176–184. doi: https://doi.org/10.1007/BF02428177
- J.O. Ramsay and B.W. Silverman, Functional Data Analysis, Springer, New York, 2005.
- L.N. Rathnayake and P.K. Choudhary, Tolerance bands for functional data, Biometrics 72 (2016), pp. 503–512. doi: https://doi.org/10.1111/biom.12434
- D. Robinson, Functional analysis and partial matching in the square root velocity framework, Ph.D. diss., Florida State University. 2012.
- A. Saha, K. Bharath, and S. Kurtek, Geometric variational approach to Bayesian inference, Journal of the American Association, To Appear, 2019.
- A. Saha and S. Kurtek, Sensitivity measures for Bayesian nonparametric density estimation models, Sankhya A, To Appear, 2019.
- A. Srivastava and I.H. Jermyn, Looking for shapes in two-dimensional, cluttered point clouds, IEEE Trans. Pattern Anal. Machine Intell. 31 (2009), pp. 1616–1629. doi: https://doi.org/10.1109/TPAMI.2008.223
- A. Srivastava, E. Klassen, S. Joshi, and I. Jermyn, Shape analysis of elastic curves in Euclidean spaces, IEEE Trans. Pattern Anal. Machine Intell. 33 (2011), pp. 1415–1428. doi: https://doi.org/10.1109/TPAMI.2010.184
- A. Srivastava and E.P. Klassen, Functional and Shape Data Analysis, Springer-Verlag, New York, 2016.
- A. Srivastava, W. Wu, S. Kurtek, E. Klassen, and J.S. Marron, Registration of functional data using Fisher-Rao metric, arXiv:1103.3817v2 [math.ST] (2011). Available at http://arxiv.org/abs/1103.3817v2.
- C.B. Storlie, M.L. Fugate, D.M. Higdon, A.V. Huzurbazar, E.G. Francois, and D.C. McHugh, Methods for characterizing and comparing populations of shock wave curves, Technometrics 55 (2013), pp. 436–449. doi: https://doi.org/10.1080/00401706.2013.805662
- Y. Sun and M.G. Genton, Functional boxplots, J. Comput. Graph. Stat. 20 (2011), pp. 316–334. doi: https://doi.org/10.1198/jcgs.2011.09224
- J.D. Tucker, Functional component analysis and regression using elastic methods, Ph.D. diss., Florida State University, 2014.
- J.D. Tucker, Functional Statistical Process Control using Elastic Methods, Proceedings of Joint Statistical Meetings, 2016.
- J.D. Tucker, W. Wu, and A. Srivastava, Generative models for functional data using phase and amplitude separation, Comput. Stat. Data Anal. 61 (2013), pp. 50–66. doi: https://doi.org/10.1016/j.csda.2012.12.001
- A. Veeraraghavan, A. Srivastava, A.K. Roy-Chowdhury, and R. Chellappa, Rate-invariant recognition of humans and their activities, IEEE Trans. Image Proc. 8 (2009), pp. 1326–1339. doi: https://doi.org/10.1109/TIP.2009.2017143
- W. Xie, S. Kurtek, K. Bharath, and Y. Sun, A geometric approach to visualization of variability in functional data, J. Amer. Stat. Assoc. 112 (2017), pp. 979–993. doi: https://doi.org/10.1080/01621459.2016.1256813
- Q. Yu, X. Lu, and J.S. Marron, Principal nested spheres for time-warped functional data analysis, J. Comput. Graph. Stat. 26 (2017), pp. 144–151. doi: https://doi.org/10.1080/10618600.2015.1115359