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Bayesian accelerated failure time models based on penalized mixtures of Gaussians: regularization and variable selection

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Bayesian accelerated failure time models based on penalized mixtures of Gaussians: regularization and variable selection

Content Provider	Springer Nature Link
Author	Konrath, Susanne Fahrmeir, Ludwig Kneib, Thomas
Copyright Year	2014
Abstract	In many biostatistical applications concerned with the analysis of duration times and especially those including high-dimensional genetic information, the following three extensions of classical accelerated failure time (AFT) models are required: (1) a flexible, nonparametric estimate of the survival time distribution, (2) a structured additive predictor including linear as well as nonlinear effects of continuous covariates and possibly further types of effects such as random or spatial effects, and (3) regularization and variable selection of high-dimensional effect vectors. Although a lot of research has dealt with these features separately, the development of AFT models combining them in a unified framework has not been considered yet. We present a Bayesian approach for modeling and inference in such flexible AFT models, incorporating a penalized Gaussian mixture error distribution, a structured additive predictor with Bayesian P-splines as a main ingredient, and Bayesian versions of ridge and LASSO as well as a spike and slab priors to enforce sparseness. Priors for regression coefficients are conditionally Gaussian, facilitating Markov chain Monte Carlo inference. The proposed model class is extensively tested in simulation studies and applied in the analysis of acute myeloid leukemia survival times considering microarray information as well as clinical covariates as prognostic factors.
Starting Page	259
Ending Page	280
Page Count	22
File Format	PDF
ISSN	18638171
Journal	AStA Advances in Statistical Analysis
Volume Number	99
Issue Number	3
e-ISSN	1863818X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-11-15
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Regularization priors Scale mixtures of normals Penalized splines Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Probability Theory and Stochastic Processes Econometrics
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Social Sciences Economics and Econometrics Analysis Modeling and Simulation

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