Functional Data Imputation
Functional Data Imputation is a statistical methodology within functional data analysis that specifically addresses the estimation of missing or unobserved segments in functional time series by leveraging the inherent smoothness, temporal dependence, and global structure of the data.
16
Publications
1.1K
Citations
49
Authors
35
Institutions
Publications per year
2017–2026
12
16
PhosR enables processing and functional analysis of phosphoproteomic data
Hani Jieun Kim, Tai-Yun Kim, Nolan J. Hoffman et al. · Cell Reports · 2021 · 125 citations · Full text
Zhicheng Chen, Hui Li, Yuequan Bao · Structural Health Monitoring · 2018 · 84 citations
Engineering, Computational Engineering, Statistical Modeling +16
Jeffrey S. Morris, Cassandra Arroyo, Brent A. Coull et al. · Journal of the American Statistical Association · 2006 · 63 citations · Full text
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35

Harbin Institute of Technology
Harbin, China
3 h-index
11 publications
494 citations

The University of Sydney
Sydney, Australia
2 h-index
14 publications
945 citations
George Washington University
Washington, United States
2 h-index
2 publications
45 citations

Ministry of Industry and Information Technology
Beijing, China
2 h-index
7 publications
233 citations
Children's Medical Research Institute
Sydney, Australia
2 h-index
8 publications
540 citations
HUN-REN Centre for Ecological Research
Budapest, Hungary
1 h-index
2 publications
94 citations
The University of Texas MD Anderson Cancer Center
Houston, United States
1 h-index
6 publications
378 citations

Eötvös Loránd University
Budapest, Hungary
1 h-index
2 publications
94 citations
1–8 of 35
3 publications · 606 citations
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SHM · 3 publications · 156 citations
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CSDA · 1 publications · 25 citations
1
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1 publications · 47 citations
1
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1 publications · 20 citations
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