About: Shrinkage (statistics)

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

About: Shrinkage (statistics) 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/describe/?url=http%3A%2F%2Fdbpedia.org%2Fresource%2FShrinkage_%28statistics%29

In statistics, shrinkage is the reduction in the effects of sampling variation. In regression analysis, a fitted relationship appears to perform less well on a new data set than on the data set used for fitting. In particular the value of the coefficient of determination 'shrinks'. This idea is complementary to overfitting and, separately, to the standard adjustment made in the coefficient of determination to compensate for the subjunctive effects of further sampling, like controlling for the potential of new explanatory terms improving the model by chance: that is, the adjustment formula itself provides "shrinkage." But the adjustment formula yields an artificial shrinkage.

Attributes	Values
rdfs:label	Shrinkage (statistics) (en)
rdfs:comment	In statistics, shrinkage is the reduction in the effects of sampling variation. In regression analysis, a fitted relationship appears to perform less well on a new data set than on the data set used for fitting. In particular the value of the coefficient of determination 'shrinks'. This idea is complementary to overfitting and, separately, to the standard adjustment made in the coefficient of determination to compensate for the subjunctive effects of further sampling, like controlling for the potential of new explanatory terms improving the model by chance: that is, the adjustment formula itself provides "shrinkage." But the adjustment formula yields an artificial shrinkage. (en)
dcterms:subject	Estimation theory Estimator
Wikipage page ID	8301951 (xsd:integer)
Wikipage revision ID	1043964432 (xsd:integer)
Link from a Wikipage to another Wikipage	Bayesian inference Sample variance Bessel's correction Bias of an estimator Ridge regression Variance Decision stump James–Stein estimator Estimation of covariance matrices Estimator Coefficient of determination Statistical inference Statistics Principal component regression Mark and recapture Estimation theory Additive smoothing Estimator Stein's example Excess kurtosis Regression analysis Regularization (mathematics) Tikhonov regularization Boosting (machine learning) Dominating estimator Ill-posed problem Maximum likelihood Mean squared error Least-squares estimation Overfitting Bias-variance tradeoff Lasso regression
Link from a Wikipage to an external page	https://cran.r-project.org/web/packages/entropy/ https://cran.r-project.org/web/packages/entropy/%7Cwork=entropy
sameAs	Shrinkage (statistics) Shrinkage (statistics) Shrinkage (statistics)
dbp:wikiPageUsesTemplate	dbt:Cite_web dbt:Reflist dbt:Short_description dbt:Stat-stub
has abstract	In statistics, shrinkage is the reduction in the effects of sampling variation. In regression analysis, a fitted relationship appears to perform less well on a new data set than on the data set used for fitting. In particular the value of the coefficient of determination 'shrinks'. This idea is complementary to overfitting and, separately, to the standard adjustment made in the coefficient of determination to compensate for the subjunctive effects of further sampling, like controlling for the potential of new explanatory terms improving the model by chance: that is, the adjustment formula itself provides "shrinkage." But the adjustment formula yields an artificial shrinkage. A shrinkage estimator is an estimator that, either explicitly or implicitly, incorporates the effects of shrinkage. In loose terms this means that a naive or raw estimate is improved by combining it with other information. The term relates to the notion that the improved estimate is made closer to the value supplied by the 'other information' than the raw estimate. In this sense, shrinkage is used to regularize ill-posed inference problems. Shrinkage is implicit in Bayesian inference and penalized likelihood inference, and explicit in James–Stein-type inference. In contrast, simple types of maximum-likelihood and least-squares estimation procedures do not include shrinkage effects, although they can be used within shrinkage estimation schemes. (en)
prov:wasDerivedFrom	wikipedia-en:Shrinkage_(statistics)?oldid=1043964432&ns=0
page length (characters) of wiki page	6690 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Shrinkage_(statistics)
is Link from a Wikipage to another Wikipage of	Bayesian vector autoregression Electricity price forecasting One in ten rule Bhramar Mukherjee Bias–variance tradeoff James–Stein estimator Empirical Bayes method Lasso (statistics) Regression toward the mean High-dimensional statistics Shrinkage List of statistics articles Shrinkage estimator Overfitting Shrinkage coefficient Shrinkage coefficients Shrinkage factor Shrinkage factors
is Wikipage redirect of	Shrinkage estimator Shrinkage coefficient Shrinkage coefficients Shrinkage factor Shrinkage factors
is Wikipage disambiguates of	Shrinkage
is foaf:primaryTopic of	wikipedia-en:Shrinkage_(statistics)

Faceted Search & Find service v1.17_git139 as of Feb 29 2024

Alternative Linked Data Documents: ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 08.03.3330 as of Mar 19 2024, on Linux (x86_64-generic-linux-glibc212), Single-Server Edition (378 GB total memory, 67 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software