| Full text | |
| Author(s): |
Total Authors: 2
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| Affiliation: | [1] Univ Estadual Campinas, Dept Stat, BR-13083970 Campinas, SP - Brazil
Total Affiliations: 1
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| Document type: | Journal article |
| Source: | ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS; v. 72, n. 5, p. 1175-1204, OCT 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Functional data analysis is ubiquitous in most areas of sciences and engineering. Several paradigms are proposed to deal with the dimensionality problem which is inherent to this type of data. Sparseness, penalization, thresholding, among other principles, have been used to tackle this issue. We discuss here a solution based on a finite-dimensional functional subspace. We employ wavelet representation of random functions to estimate this finite dimension and successfully model a time series of curves. The proposed method is shown to have nice asymptotic properties. Moreover, the wavelet representation permits the use of several bootstrap procedures, and it results in faster computing algorithms. Besides the theoretical and computational properties, some simulation studies and an application to real data are provided. (AU) | |
| FAPESP's process: | 16/24469-6 - Wavelet Funcional Data Analysis: Foundations and Applications |
| Grantee: | Rodney Vasconcelos Fonseca |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 13/00506-1 - Time series, wavelets and functional data analysis |
| Grantee: | Pedro Alberto Morettin |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/04654-9 - Time series, wavelets and high dimensional data |
| Grantee: | Pedro Alberto Morettin |
| Support Opportunities: | Research Projects - Thematic Grants |