High-dimensional data: a fascinating statistical challenge
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Cited by:
- Miyazaki, Izuru, 2023. "Recovery of partly sparse and dense signals," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
- Simone Vantini, 2012. "On the definition of phase and amplitude variability in functional data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de EstadÃstica e Investigación Operativa, vol. 21(4), pages 676-696, December.
- Shi Shen & Changxiu Cheng & Changqing Song & Jing Yang & Shanli Yang & Kai Su & Lihua Yuan & Xiaoqiang Chen, 2018. "Spatial distribution patterns of global natural disasters based on biclustering," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(3), pages 1809-1820, July.
- Bouabsa Wahiba, 2023. "The Estimating of the Conditional Density with Application to the Mode Function in Scalar-On-Function Regression Structure: Local Linear Approach with Missing at Random," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 27(1), pages 17-32, March.
- Llop, P. & Forzani, L. & Fraiman, R., 2011. "On local times, density estimation and supervised classification from functional data," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 73-86, January.
- Gheriballah, Abdelkader & Laksaci, Ali & Sekkal, Soumeya, 2013. "Nonparametric M-regression for functional ergodic data," Statistics & Probability Letters, Elsevier, vol. 83(3), pages 902-908.
- Zhou, Jie, 2011. "Maximum likelihood ratio test for the stability of sequence of Gaussian random processes," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2114-2127, June.
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