Functional Moving Average Models is a class of functional time series models that represents a stochastic process as a linear combination of current and past functional innovations, distinguished by its use of Hilbert space-valued noise terms and integral operator kernels to capture temporal dependence in infinite-dimensional settings.
5
Publications
440
Citations
10
Authors
8
Institutions
Publications per year
2017–2026
1
Publications
5
Functional Data Analysis. Springer Series in Statistics
J. O. Ramsay, B.W. Silvermann · Biometrical Journal · 1998 · 387 citations
Statistical Inference for Functional Time Series
Jie Li, Lijian Yang · Statistica Sinica · 2021 · 16 citations · Full text
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Authors
10
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- SD
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- GS
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- MM
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- JO
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1–5 of 10
Institutions
8
University of Patras
Pátrai, Greece
1 h-index
2 publications
32 citations
California Polytechnic State University
San Luis Obispo, United States
1 h-index
1 publications
11 citations
Institute for Advanced Study
Berlin, Germany
1 h-index
1 publications
10 citations
University of California, Berkeley
Berkeley, United States
1 h-index
1 publications
11 citations
Technical University of Munich
Munich, Germany
1 h-index
1 publications
10 citations
Springer Nature (Germany)
Berlin, Germany
1 h-index
2 publications
774 citations
Helmholtz-Institute Ulm
Ulm, Germany
1 h-index
1 publications
10 citations
Tsinghua University
Beijing, China
1 h-index
2 publications
32 citations
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Venues
- Statistica Sinica
1 publications · 16 citations
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- Extremes
1 publications · 10 citations
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- Biometrical Journal
1 publications · 387 citations
1
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- Ecological Modelling
1 publications · 11 citations
1
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- Journal of Dynamic Systems Measurement and Control
1 publications · 16 citations
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