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Titel:

Laplace Approximation in High-Dimensional Bayesian Regression

Dokumenttyp:
Buchbeitrag
Autor(en):
Barber, Rina Foygel; Drton, Mathias; Tan, Kean Ming
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...     »
Seitenangaben Beitrag:
15-36
Dewey-Dezimalklassifikation:
510 Mathematik
Buchtitel:
Statistical Analysis for High-Dimensional Data
Titelzusatz:
The Abel Symposium 2014
Verlag / Institution:
Springer International Publishing
Publikationsdatum:
17.02.2016
Jahr:
2016
Quartal:
1. Quartal
Jahr / Monat:
2016-02
Monat:
Feb
Seiten/Umfang:
15-36
Print-ISBN:
97833192709759783319270999
Sprache:
en
DOI:
doi:10.1007/978-3-319-27099-9_2
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