Bayesian Smoothing, Shrinkage and Variable Selection in Hazard Regression View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Susanne Konrath , Ludwig Fahrmeir , Thomas Kneib

ABSTRACT

This contribution deals with a unified Bayesian framework to combine regularization of high-dimensional linear covariate effects and semiparametric smoothing of nonlinear functional effects for a broad class of hazard regression models. While penalized splines with conditionally Gaussian smoothness priors form the basis for estimating nonparametric and flexible time-varying effects, regularization of high-dimensional covariate vectors is based on scale mixture of normals priors, including among others the Bayesian ridge and lasso as well as a spike and slab prior for shrinkage variances. This class of priors allows us to keep a conditionally Gaussian prior for regression coefficients on the predictor stage of the model but introduces suitable mixture distributions for the Gaussian variance to achieve regularization. The scale mixture property allows to device general and adaptive Markov chain Monte Carlo simulation algorithms for fitting a variety of hazard regression models. In particular, unifying Metropolis-Hastings-algorithms based on iteratively weighted least squares proposals can be employed both for regularization and penalized semiparametric function estimation. We demonstrate performance through simulation studies and an application to data on acute myeloid leukemia (AML) survival. More... »

PAGES

149-170

References to SciGraph publications

Book

TITLE

Robustness and Complex Data Structures

ISBN

978-3-642-35493-9
978-3-642-35494-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-35494-6_10

DOI

http://dx.doi.org/10.1007/978-3-642-35494-6_10

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036107133


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