About: Spike-and-slab regression

Facets (new session)
Description
Metadata
Settings
- Rule:
- Inverse Functional Properties:
- "Same As":

About: Spike-and-slab regression Goto Sponge NotDistinct Permalink

An Entity of Type : owl:Thing, within Data Space : dbpedia.demo.openlinksw.com associated with source document(s)
QRcode icon

http://dbpedia.demo.openlinksw.com/c/2zSrUdWE4C

Spike-and-slab regression is a type of Bayesian linear regression in which a particular hierarchical prior distribution for the regression coefficients is chosen such that only a subset of the possible regressors is retained. The technique that is particularly useful when the number of possible predictors is larger than the number of observations. The idea of the spike-and-slab model was originally proposed by Mitchell & Beauchamp (1988). The approach was further significantly developed by Madigan & Raftery (1994) and George & McCulloch (1997). The final adjustments to the model were done by Ishwaran & Rao (2005).

Attributes	Values
rdfs:label	Spike-and-slab regression (en)
rdfs:comment	Spike-and-slab regression is a type of Bayesian linear regression in which a particular hierarchical prior distribution for the regression coefficients is chosen such that only a subset of the possible regressors is retained. The technique that is particularly useful when the number of possible predictors is larger than the number of observations. The idea of the spike-and-slab model was originally proposed by Mitchell & Beauchamp (1988). The approach was further significantly developed by Madigan & Raftery (1994) and George & McCulloch (1997). The final adjustments to the model were done by Ishwaran & Rao (2005). (en)
dct:subject	Bayesian inference Machine learning Bayesian statistics
Wikipage page ID	50227596 (xsd:integer)
Wikipage revision ID	1110000231 (xsd:integer)
Link from a Wikipage to another Wikipage	Bayesian linear regression Bernoulli distribution Design matrix Bayesian inference Feature selection Machine learning Lasso (statistics) Normal distribution Posterior probability Prior probability Bayesian statistics Markov chain Monte Carlo Bayesian model averaging Hierarchical prior distribution
sameAs	Spike-and-slab regression Spike-and-slab regression
dbp:wikiPageUsesTemplate	dbt:Cite_book dbt:Pi dbt:Reflist dbt:Short_description
has abstract	Spike-and-slab regression is a type of Bayesian linear regression in which a particular hierarchical prior distribution for the regression coefficients is chosen such that only a subset of the possible regressors is retained. The technique that is particularly useful when the number of possible predictors is larger than the number of observations. The idea of the spike-and-slab model was originally proposed by Mitchell & Beauchamp (1988). The approach was further significantly developed by Madigan & Raftery (1994) and George & McCulloch (1997). The final adjustments to the model were done by Ishwaran & Rao (2005). (en)
prov:wasDerivedFrom	wikipedia-en:Spike-and-slab_regression?oldid=1110000231&ns=0
page length (characters) of wiki page	6248 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Spike-and-slab_regression
is Link from a Wikipage to another Wikipage of	Spike-and-slab variable selection Spike and slab variable selection
is Wikipage redirect of	Spike-and-slab variable selection Spike and slab variable selection
is foaf:primaryTopic of	wikipedia-en:Spike-and-slab_regression

Faceted Search & Find service v1.17_git147 as of Sep 06 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3332 as of Dec 5 2024, on Linux (x86_64-generic-linux-glibc212), Single-Server Edition (378 GB total memory, 64 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software