Renewable composite quantile method and algorithm for nonparametric models with streaming data-Reference-Cited by-同舟云学术

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Renewable composite quantile method and algorithm for nonparametric models with streaming data

Published:2023-11-17 Issue:1 Volume:34 Page:
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

Chen Yan,Fang Shuixin,Lin Lu

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s11222-023-10352-x.pdf

Reference40 articles.

1. Ashfahani, A., Pratama, M.: Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments, pp. 666–674 (2019)

2. Bednar, J., Watt, T.: Alpha-trimmed means and their relationship to median filters. IEEE Trans. Acoust. Speech Signal Process. 32(1), 145–153 (1984). https://doi.org/10.1109/TASSP.1984.1164279

3. Bickel, P.J., Lehmann, E.L.: Descriptive statistics for nonparametric models. II. Location. Ann. Stat. 3(5), 1045–1069 (1975)

4. Boente, G., Fraiman, R.: Local $$L$$-estimators for nonparametric regression under dependence. J. Nonparametr. Statist. 4(1), 91–101 (1994). https://doi.org/10.1080/10485259408832603

5. Bucak, S.S., Gunsel, B.: Incremental subspace learning via non-negative matrix factorization. Pattern Recogn. 42(5), 788–797 (2009). https://doi.org/10.1016/j.patcog.2008.09.002

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