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Document type:
Buchbeitrag 
Author(s):
Barber, Rina Foygel; Drton, Mathias; Tan, Kean Ming 
Title:
Laplace Approximation in High-Dimensional Bayesian Regression 
Pages contribution:
15-36 
Abstract:
We consider Bayesian variable selection in sparse high-dimensional regression, where the number of covariates p may be large relative to the sample size n, but at most a moderate number q of covariates are active. Specifically, we treat generalized linear models. For a single fixed sparse model with well-behaved prior distribution, classical theory proves that the Laplace approximation to the marginal likelihood of the model is accurate for sufficiently large sample size n. We extend this theory...    »
 
Dewey Decimal Classification:
510 Mathematik 
Book title:
Statistical Analysis for High-Dimensional Data 
Book subtitle:
The Abel Symposium 2014 
Publisher:
Springer International Publishing 
Date of publication:
17.02.2016 
Year:
2016 
Quarter:
1. Quartal 
Year / month:
2016-02 
Month:
Feb 
Pages:
15-36 
Print-ISBN:
97833192709759783319270999 
Language:
en