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Spline Models for Observational Data
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“As shown in [18], Equation (1) can be analytically solved efficiently; details are provided in Appendix 9.1.”
From Learning from Demonstrations Through the Use of Non-rigid Registration (2016) · cites this paper as [18]
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Metadata
- Authors
- Wahba, G.
- Affiliations
- University of Wisconsin–Madison (US)
- Corresponding author
- Wahba, Grace
- Venue
- Society for Industrial and Applied Mathematics eBooks
- Published
- 1990-01-01
- OpenAlex
- W4298876635
- Cited by
- 4,184
- References
- 0
- Citations by year
- 2026: 89 · 2025: 147 · 2024: 135 · 2023: 141 · 2022: 156 · 2021: 191 · 2020: 184 · 2019: 154 · 2018: 165 · 2017: 156 · 2016: 140 · 2015: 152 · 2014: 156 · 2013: 161 · 2012: 163
- FWCI
- 17.89
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- Society for Industrial and Applied Mathematics eBooks (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Applied Mathematics › Statistical and numerical algorithms
- Keywords
- spline models, smoothing splines, nonparametric regression, penalized splines, bandwidth selection, loess
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