The DOI matches its Crossref record.
Nonparametric Regression and Generalized Linear Models: A roughness penalty approach
Abstract
In recent years, there has been a great deal of interest and activity in the general area of nonparametric smoothing in statistics. This monograph concentrates on the roughness penalty method and shows how this technique provides a unifying approach to a wide range of smoothing problems. The method allows parametric assumptions to be realized in re
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Cited by
The analysis of designed experiments and longitudinal data by using smoothing splines
Verbyla, A. P., Cullis, B. R., Kenward, M. G., & Welham, S. J. (1999) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/1467-9876.00154 · Cited by 570
Heaton, T. J., Blaauw, M., Blackwell, P. G., Bronk Ramsey, C., Reimer, P. J., & Scott, E. M. (2020) · Radiocarbon · doi:10.1017/rdc.2020.46 · Cited by 124
Metadata
- Authors
- Green, P. J., & Silverman, B. W.
- Venue
- Chapman & Hall/CRC eBooks
- Publisher
- Chapman and Hall/CRC
- Published
- 1993-05-01
- OpenAlex
- W2103038618
- Cited by
- 510
- References
- 0
- FWCI
- 0.35
- Type
- book
- Language
- en
- Open access
- closed
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- Green, P. J., & Silverman, B. W. (1993). Nonparametric Regression and Generalized Linear Models: A roughness penalty approach. Chapman & Hall/CRC eBooks. https://doi.org/10.1201/b15710
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
