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Penalized Likelihood for General Semi-Parametric Regression Models
Abstract
This paper examines maximum penalized likelihood estimation in the context of general regression problems, characterized as probability models with composite; likelihood functions. The emphasis is on the common situation where a parametric model is considered satisfactory but for inhomogeneity with respect to a few extra variables. A finite-dimensional formulation is adopted, using a suitable set of basis functions. Appropriate definitions of deviance, degrees of freedom, and residual are provided, and the method of cross-validation for choice of the tuning constant is discussed. Quadratic approximations are derived for all the required statistics. Additional keywords: algorithms; smoothing; goodness of fit tests; nonlinear repression. (Author)
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Craven, P., & Wahba, G. (1978) · Numerische Mathematik · doi:10.1007/bf01404567 · Cited by 3,388
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Sanderson, M. J. (1997) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a025731 · Cited by 1,156
Linear Smoothers and Additive Models
Buja, A., Hastie, T., & Tibshirani, R. (1989) · The Annals of Statistics · doi:10.1214/aos/1176347115 · Cited by 989
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
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Metadata
- Authors
- Green, P. J.
- Venue
- International Statistical Review / Revue Internationale de Statistique
- Publisher
- JSTOR
- Published
- 1987-12
- Volume
- 55
- Issue
- 3
- Pages
- 245
- ISSN
- 0306-7734 (ISSN-L)
- OpenAlex
- W1997407470
- Cited by
- 286
- References
- 1
- FWCI
- 7.10
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
