Dimensionality reduction when data are density functions

Citations

Cited by:

1. Berrendero, José R. & Cuevas, Antonio & Pateiro-López, Beatriz, 2016. "Shape classification based on interpoint distance distributions," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 237-247.
2. Hron, K. & Menafoglio, A. & Templ, M. & Hrůzová, K. & Filzmoser, P., 2016. "Simplicial principal component analysis for density functions in Bayes spaces," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 330-350.
3. Won-Ki Seo, 2020. "Functional Principal Component Analysis for Cointegrated Functional Time Series," Papers 2011.12781, arXiv.org, revised Apr 2023.
4. Calò, Daniela G. & Montanari, Angela & Viroli, Cinzia, 2014. "A hierarchical modeling approach for clustering probability density functions," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 79-91.
5. Yoshiyuki ARATA, 2017. "A Functional Linear Regression Model in the Space of Probability Density Functions," Discussion papers 17015, Research Institute of Economy, Trade and Industry (RIETI).
6. Tadao Hoshino, 2024. "Functional Spatial Autoregressive Models," Papers 2402.14763, arXiv.org, revised Oct 2024.
7. Bongiorno, Enea G. & Goia, Aldo, 2019. "Describing the concentration of income populations by functional principal component analysis on Lorenz curves," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 10-24.
8. S. Barahona & P. Centella & X. Gual-Arnau & M. V. Ibáñez & A. Simó, 2020. "Supervised classification of geometrical objects by integrating currents and functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(3), pages 637-660, September.
9. Tomohiro Ando & Tadao Hoshino, 2025. "Functional Network Autoregressive Models for Panel Data," Papers 2502.13431, arXiv.org.
10. Karel Hron & Jitka Machalová & Alessandra Menafoglio, 2023. "Bivariate densities in Bayes spaces: orthogonal decomposition and spline representation," Statistical Papers, Springer, vol. 64(5), pages 1629-1667, October.
11. J. Machalová & K. Hron & G.S. Monti, 2016. "Preprocessing of centred logratio transformed density functions using smoothing splines," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(8), pages 1419-1435, June.
12. Petersen, Alexander & Zhang, Chao & Kokoszka, Piotr, 2022. "Modeling Probability Density Functions as Data Objects," Econometrics and Statistics, Elsevier, vol. 21(C), pages 159-178.
13. Martínez-Camblor, Pablo & Corral, Norberto, 2011. "Repeated measures analysis for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3244-3256, December.
14. Genest, Christian & Hron, Karel & Nešlehová, Johanna G., 2023. "Orthogonal decomposition of multivariate densities in Bayes spaces and relation with their copula-based representation," Journal of Multivariate Analysis, Elsevier, vol. 198(C).
15. Berrendero, J.R. & Justel, A. & Svarc, M., 2011. "Principal components for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2619-2634, September.
16. Delicado, Pedro & Vieu, Philippe, 2015. "Optimal level sets for bivariate density representation," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 1-18.
17. Jitka Machalová & Renáta Talská & Karel Hron & Aleš Gába, 2021. "Compositional splines for representation of density functions," Computational Statistics, Springer, vol. 36(2), pages 1031-1064, June.
18. Pedro Delicado, 2024. "Comments on: Shape-based functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(1), pages 62-65, March.
19. Seo, Won-Ki & Beare, Brendan K., 2019. "Cointegrated linear processes in Bayes Hilbert space," Statistics & Probability Letters, Elsevier, vol. 147(C), pages 90-95.
20. Talská, R. & Menafoglio, A. & Machalová, J. & Hron, K. & Fišerová, E., 2018. "Compositional regression with functional response," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 66-85.
21. Kokoszka, Piotr & Miao, Hong & Petersen, Alexander & Shang, Han Lin, 2019. "Forecasting of density functions with an application to cross-sectional and intraday returns," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1304-1317.
22. Menafoglio, Alessandra & Petris, Giovanni, 2016. "Kriging for Hilbert-space valued random fields: The operatorial point of view," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 84-94.
23. Shang, Han Lin & Haberman, Steven & Xu, Ruofan, 2022. "Multi-population modelling and forecasting life-table death counts," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 239-253.
24. Zhang, Zhen & Müller, Hans-Georg, 2011. "Functional density synchronization," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2234-2249, July.
25. Hsin‐wen Chang & Ian W. McKeague, 2022. "Empirical likelihood‐based inference for functional means with application to wearable device data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1947-1968, November.
26. Angela Montanari & Daniela Calò, 2013. "Model-based clustering of probability density functions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 7(3), pages 301-319, September.
27. Epifanio, Irene & Ventura-Campos, Noelia, 2011. "Functional data analysis in shape analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2758-2773, September.