shrinkage strip - significado y definición. Qué es shrinkage strip
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Qué (quién) es shrinkage strip - definición

PHENOMENON IN STATISTICS
Shrinkage estimator; Shrinkage factor; Shrinkage factors; Shrinkage coefficient; Shrinkage coefficients

Shrinkage (statistics)         
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.
Caprivi Strip         
  • Map of the Caprivi
  • Georg Leo Graf von Caprivi de Caprera de Montecuccoli]], who gave his name to the Caprivi Strip
  • Village in the Caprivi Strip
GEOGRAPHICAL AREA OF NORTH-EASTERN NAMIBIA
Caprivi strip; Okavango Strip
The Caprivi Strip, also known simply as Caprivi, is a geographic salient protruding from the northeastern corner of Namibia. It is surrounded by Botswana to the south and Angola and Zambia to the north.
Dimensional stability (fabric)         
  • Shrinkage measuring template, scale and marker
CHANGE IN SIZE OF TEXTILE FABRICS AFTER WASHING
Shrinkage (fabric)
Dimensional stability (in fabric) is the change of dimensions in textile products when they are washed or relaxed. The change is always expressed relative to the dimensions before the exposure of washing or relaxing.

Wikipedia

Shrinkage (statistics)

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.