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Subject Item
dbr:Shrinkage_(statistics)
rdfs:label
Shrinkage (statistics)
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.
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dbc:Estimator dbc:Estimation_theory
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8301951
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1043964432
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dbr:Statistical_inference dbr:James–Stein_estimator dbr:Bias_of_an_estimator dbr:Ridge_regression dbr:Dominating_estimator dbr:Coefficient_of_determination dbr:Stein's_example dbr:Regression_analysis dbr:Maximum_likelihood dbc:Estimator dbr:Estimator dbr:Bias-variance_tradeoff dbr:Bessel's_correction dbr:Principal_component_regression dbr:Least-squares_estimation dbr:Boosting_(machine_learning) dbr:Excess_kurtosis dbr:Additive_smoothing dbr:Bayesian_inference dbc:Estimation_theory dbr:Mark_and_recapture dbr:Estimation_of_covariance_matrices dbr:Overfitting dbr:Decision_stump dbr:Regularization_(mathematics) dbr:Ill-posed_problem dbr:Tikhonov_regularization dbr:Variance dbr:Mean_squared_error dbr:Lasso_regression dbr:Sample_variance dbr:Statistics
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dbo: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.
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