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Smoothing parameter and model selection for general smooth models
2016 (English)In: Journal of the American Statistical Association, ISSN 0162-1459, E-ISSN 1537-274X, Vol. 111, no 516, p. 1548-1563Article in journal (Refereed) Published
Abstract [en]

This article discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By construction the method is numerically stable and convergent, and enables smoothing parameter uncertainty to be quantified. The latter enables us to fix a well known problem with AIC for such models, thereby improving the range of model selection tools available. The smooth functions are represented by reduced rank spline like smoothers, with associated quadratic penalties measuring function smoothness. Model estimation is by penalized likelihood maximization, where the smoothing parameters controlling the extent of penalization are estimated by Laplace approximate marginal likelihood. The methods cover, for example, generalized additive models for nonexponential family responses (e.g., beta, ordered categorical, scaled t distribution, negative binomial and Tweedie distributions), generalized additive models for location scale and shape (e.g., two stage zero inflation models, and Gaussian location scale models), Cox proportional hazards models and multivariate additive models. The framework reduces the implementation of new model classes to the coding of some standard derivatives of the log-likelihood. Supplementary materials for this article are available online.

Place, publisher, year, edition, pages
2016. Vol. 111, no 516, p. 1548-1563
Keywords [en]
Additive model, AIC, Distributional regression, GAM, Location scale and shape model, Ordered categorical regression, Penalized regression spline, REML, Smooth Cox model, Smoothing parameter uncertainty, Statistical algorithm, Tweedie distribution
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-160060DOI: 10.1080/01621459.2016.1180986ISI: 000391900700023OAI: oai:DiVA.org:umu-160060DiVA, id: diva2:1323513
Available from: 2019-06-12 Created: 2019-06-12 Last updated: 2019-06-13Bibliographically approved

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Pya Arnqvist, Natalya

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CiteExportLink to record
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  • apa
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  • de-DE
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Output format
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  • asciidoc
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