The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Generalized structured additive regression based on Bayesian P-splines
Open accessAbstract
Generalized additive models (GAM) for modeling nonlinear effects of continuous covariates are now well established tools for the applied statistician. A Bayesian version of GAM's and extensions to generalized structured additive regression (STAR) are developed. One or two dimensional P-splines are used as the main building block. Inference relies on Markov chain Monte Carlo (MCMC) simulation techniques, and is either based on iteratively weighted least squares (IWLS) proposals or on latent utility representations of (multi)categorical regression models. The approach covers the most common univariate response distributions, e.g., the binomial, Poisson or gamma distribution, as well as multicategorical responses. For the first time, Bayesian semiparametric inference for the widely used multinomial logit model is presented. Two applications on the forest health status of trees and a space-time analysis of health insurance data demonstrate the potential of the approach for realistic modeling of complex problems. Software for the methodology is provided within the public domain package BayesX.
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Cited by
Smoothing Parameter and Model Selection for General Smooth Models
Wood, S. N., Pya, N., & Säfken, B. (2016) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1180986 · Cited by 1,312
Non-life rate-making with Bayesian GAMs
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Metadata
- Authors
- Brezger, A., & Lang, S.
- Venue
- Computational Statistics & Data Analysis
- Publisher
- Elsevier BV
- Published
- 2006-02
- Volume
- 50
- Issue
- 4
- Pages
- 967-991
- ISSN
- 0167-9473 (ISSN-L)
- OpenAlex
- W2134080536
- Cited by
- 371
- References
- 66
- FWCI
- 11.48
- Type
- journal article
- Language
- en
- Open access
- green
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