Tree-based boosting with functional data
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2023/05/22
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Journal
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Indian UGC (journal)
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Refrences64
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Xiaomeng Ju ORCID (unauthenticated)
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Matías Salibián-Barrera
Cite
Ju, Xiaomeng, and Matías Salibián-Barrera. “Tree-Based Boosting With Functional Data”. Computational Statistics, vol. 39, no. 3, 2023, pp. 1587-20, https://doi.org/10.1007/s00180-023-01364-2.
Ju, X., & Salibián-Barrera, M. (2023). Tree-based boosting with functional data. Computational Statistics, 39(3), 1587-1620. https://doi.org/10.1007/s00180-023-01364-2
Ju, Xiaomeng, and Matías Salibián-Barrera. “Tree-Based Boosting With Functional Data”. Computational Statistics 39, no. 3 (2023): 1587-1620. https://doi.org/10.1007/s00180-023-01364-2.
Ju X, Salibián-Barrera M. Tree-based boosting with functional data. Computational Statistics. 2023;39(3):1587-620.
Journal Categories
Science
Mathematics
Science
Mathematics
Probabilities
Mathematical statistics
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Robust functional principal components for sparse longitudinal data | METRON |
| 8 | 2021 |
| Nonparametric modelling for functional data: selected survey and tracks for future | Statistics |
| 85 | 2018 |
| 10.1177/1471082X16681317 | 2017 | |||
Random forests for functional covariates | Journal of Chemometrics |
| 13 | 2016 |
| Fitting Linear Mixed-Effects Models Usinglme4 | Journal of Statistical Software |
| 46,322 | 2015 |
Refrences Analysis
The category
Science: Mathematics: Probabilities. Mathematical statistics 22
is the most frequently represented among the references in this article. It primarily includes studies from Journal of the American Statistical Association and Statistics. The chart below illustrates the number of referenced publications per year.