scholarly journals Variable selection in classification for multivariate functional data

2019 ◽  
Vol 481 ◽  
pp. 445-462 ◽  
Author(s):  
Rafael Blanquero ◽  
Emilio Carrizosa ◽  
Asunción Jiménez-Cordero ◽  
Belén Martín-Barragán
2019 ◽  
Vol 20 (2) ◽  
pp. 123-138
Author(s):  
Tomasz Górecki ◽  
Mirosław Krzyśko ◽  
Waldemar Wołyński

2020 ◽  
Vol 35 (3) ◽  
pp. 1101-1131
Author(s):  
Amandine Schmutz ◽  
Julien Jacques ◽  
Charles Bouveyron ◽  
Laurence Chèze ◽  
Pauline Martin

2020 ◽  
Author(s):  
Antoni Torres-Signes ◽  
M. Pilar Frías ◽  
María D.Ruiz-Medina

Abstract This paper presents a multivariate functional data statistical approach, for spatiotemporal prediction of COVID-19 mortality counts. Specifically, spatial heterogeneous nonlinear parametric functional regression trend model fitting is first implemented. Classical and Bayesian infinite-dimensional log-Gaussian linear residual correlation analysis is then applied. The nonlinear regression predictor of the mortality risk is combined with the plug-in predictor of the multiplicative error term. An empirical model ranking, based on random K-fold validation, is established for COVID-19 mortality risk forecasting and assessment, involving Machine Learning (ML) models, and the adopted Classical and Bayesian semilinear estimation approach. This empirical analysis also determines the ML models favored by the spatial multivariate Functional Data Analysis (FDA) framework. The results could be extrapolated to other countries.


2021 ◽  
Author(s):  
Wenlin Dai ◽  
Stavros Athanasiadis ◽  
Tomáš Mrkvička

Clustering is an essential task in functional data analysis. In this study, we propose a framework for a clustering procedure based on functional rankings or depth. Our methods naturally combine various types of between-cluster variation equally, which caters to various discriminative sources of functional data; for example, they combine raw data with transformed data or various components of multivariate functional data with their covariance. Our methods also enhance the clustering results with a visualization tool that allows intrinsic graphical interpretation. Finally, our methods are model-free and nonparametric and hence are robust to heavy-tailed distribution or potential outliers. The implementation and performance of the proposed methods are illustrated with a simulation study and applied to three real-world applications.


2019 ◽  
Vol 49 (18) ◽  
pp. 4506-4519
Author(s):  
Zofia Hanusz ◽  
Mirosław Krzyśko ◽  
Rafał Nadulski ◽  
Łukasz Waszak

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