fdasrvf: Elastic Functional Data Analysis
In fdasrvf: Elastic Functional Data Analysis
fdasrvf R Documentation
Elastic Functional Data Analysis
Description
A library for functional data analysis using the square root velocity
framework which performs pair-wise and group-wise alignment as well as
modeling using functional component analysis.
References
Srivastava, A., Wu, W., Kurtek, S., Klassen, E., Marron, J. S.,
May 2011. Registration of functional data using Fisher-Rao metric,
arXiv:1103.3817v2.
Tucker, J. D., Wu, W., Srivastava, A., Generative models for
functional data using phase and amplitude separation, Computational
Statistics and Data Analysis (2012), 10.1016/j.csda.2012.12.001.
J. D. Tucker, W. Wu, and A. Srivastava, Phase-amplitude
separation of proteomics data using extended Fisher-Rao metric, Electronic
Journal of Statistics, Vol 8, no. 2. pp 1724-1733, 2014.
J. D. Tucker, W. Wu, and A. Srivastava, “Analysis of signals
under compositional noise with applications to SONAR data," IEEE Journal of
Oceanic Engineering, Vol 29, no. 2. pp 318-330, Apr 2014.
Tucker, J. D. 2014, Functional Component Analysis and Regression
using Elastic Methods. Ph.D. Thesis, Florida State University.
Robinson, D. T. 2012, Function Data Analysis and Partial Shape
Matching in the Square Root Velocity Framework. Ph.D. Thesis, Florida State
University.
Huang, W. 2014, Optimization Algorithms on Riemannian Manifolds
with Applications. Ph.D. Thesis, Florida State University.
Cheng, W., Dryden, I. L., and Huang, X. (2016). Bayesian
registration of functions and curves. Bayesian Analysis, 11(2), 447-475.
Srivastava, A., Klassen, E., Joshi, S., Jermyn, I., (2011). Shape
analysis of elastic curves in euclidean spaces. Pattern Analysis and
Machine Intelligence, IEEE Transactions on 33 (7), 1415-1428.
Cheng, W., Dryden, I. L., and Huang, X. (2016). Bayesian
registration of functions and curves. Bayesian Analysis, 11(2), 447-475.
W. Xie, S. Kurtek, K. Bharath, and Y. Sun, A geometric approach
to visualization of variability in functional data, Journal of American
Statistical Association 112 (2017), pp. 979-993.
Lu, Y., R. Herbei, and S. Kurtek, 2017: Bayesian registration of
functions with a Gaussian process prior. Journal of Computational and
Graphical Statistics, 26, no. 4, 894–904.
Lee, S. and S. Jung, 2017: Combined analysis of amplitude and
phase variations in functional data. arXiv:1603.01775, 1–21.
J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic Functional
Principal Component Regression,” Statistical Analysis and Data Mining, vol.
12, no. 2, pp. 101-115, 2019.
J. D. Tucker, J. R. Lewis, C. King, and S. Kurtek, “A Geometric
Approach for Computing Tolerance Bounds for Elastic Functional Data,”
Journal of Applied Statistics, 10.1080/02664763.2019.1645818, 2019.
T. Harris, J. D. Tucker, B. Li, and L. Shand, "Elastic depths for
detecting shape anomalies in functional data," Technometrics,
10.1080/00401706.2020.1811156, 2020.
J. D. Tucker and D. Yarger, “Elastic Functional Changepoint
Detection of Climate Impacts from Localized Sources”, Envirometrics,
10.1002/env.2826, 2023.
fdasrvf documentation built on Nov. 19, 2023, 1:09 a.m.
R Package Documentation
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| fdasrvf | R Documentation |
Elastic Functional Data Analysis
Description
A library for functional data analysis using the square root velocity framework which performs pair-wise and group-wise alignment as well as modeling using functional component analysis.
References
Srivastava, A., Wu, W., Kurtek, S., Klassen, E., Marron, J. S., May 2011. Registration of functional data using Fisher-Rao metric, arXiv:1103.3817v2.
Tucker, J. D., Wu, W., Srivastava, A., Generative models for functional data using phase and amplitude separation, Computational Statistics and Data Analysis (2012), 10.1016/j.csda.2012.12.001.
J. D. Tucker, W. Wu, and A. Srivastava, Phase-amplitude separation of proteomics data using extended Fisher-Rao metric, Electronic Journal of Statistics, Vol 8, no. 2. pp 1724-1733, 2014.
J. D. Tucker, W. Wu, and A. Srivastava, “Analysis of signals under compositional noise with applications to SONAR data," IEEE Journal of Oceanic Engineering, Vol 29, no. 2. pp 318-330, Apr 2014.
Tucker, J. D. 2014, Functional Component Analysis and Regression using Elastic Methods. Ph.D. Thesis, Florida State University.
Robinson, D. T. 2012, Function Data Analysis and Partial Shape Matching in the Square Root Velocity Framework. Ph.D. Thesis, Florida State University.
Huang, W. 2014, Optimization Algorithms on Riemannian Manifolds with Applications. Ph.D. Thesis, Florida State University.
Cheng, W., Dryden, I. L., and Huang, X. (2016). Bayesian registration of functions and curves. Bayesian Analysis, 11(2), 447-475.
Srivastava, A., Klassen, E., Joshi, S., Jermyn, I., (2011). Shape analysis of elastic curves in euclidean spaces. Pattern Analysis and Machine Intelligence, IEEE Transactions on 33 (7), 1415-1428.
Cheng, W., Dryden, I. L., and Huang, X. (2016). Bayesian registration of functions and curves. Bayesian Analysis, 11(2), 447-475.
W. Xie, S. Kurtek, K. Bharath, and Y. Sun, A geometric approach to visualization of variability in functional data, Journal of American Statistical Association 112 (2017), pp. 979-993.
Lu, Y., R. Herbei, and S. Kurtek, 2017: Bayesian registration of functions with a Gaussian process prior. Journal of Computational and Graphical Statistics, 26, no. 4, 894–904.
Lee, S. and S. Jung, 2017: Combined analysis of amplitude and phase variations in functional data. arXiv:1603.01775, 1–21.
J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic Functional Principal Component Regression,” Statistical Analysis and Data Mining, vol. 12, no. 2, pp. 101-115, 2019.
J. D. Tucker, J. R. Lewis, C. King, and S. Kurtek, “A Geometric Approach for Computing Tolerance Bounds for Elastic Functional Data,” Journal of Applied Statistics, 10.1080/02664763.2019.1645818, 2019.
T. Harris, J. D. Tucker, B. Li, and L. Shand, "Elastic depths for detecting shape anomalies in functional data," Technometrics, 10.1080/00401706.2020.1811156, 2020.
J. D. Tucker and D. Yarger, “Elastic Functional Changepoint Detection of Climate Impacts from Localized Sources”, Envirometrics, 10.1002/env.2826, 2023.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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