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Title:

Laplace Approximation in High-Dimensional Bayesian Regression

Document type:
Buchbeitrag
Author(s):
Barber, Rina Foygel; Drton, Mathias; Tan, Kean Ming
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
DOI:
doi:10.1007/978-3-319-27099-9_2
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