19,99 €
inkl. MwSt.
Versandfertig in 6-10 Tagen
10 °P sammeln
Andere Kunden interessierten sich auch für
![On Shrinkage Estimator for the Shape Parameter of Generalized Rayleigh]( "On Shrinkage Estimator for the Shape Parameter of Generalized Rayleigh")
On Shrinkage Estimator for the Shape Parameter of Generalized Rayleigh30,99 €![Analytical Study of Bayesian Shrinkage and Robust in Weibull Distribut]( "Analytical Study of Bayesian Shrinkage and Robust in Weibull Distribut")
Analytical Study of Bayesian Shrinkage and Robust in Weibull Distribut32,99 €![Rank-Based Methods for Shrinkage and Selection]( "Rank-Based Methods for Shrinkage and Selection")
Rank-Based Methods for Shrinkage and Selection149,99 €![Improving Efficiency by Shrinkage]( "Improving Efficiency by Shrinkage")
Improving Efficiency by Shrinkage496,99 €![Shrinkage Estimation]( "Shrinkage Estimation")
Shrinkage Estimation103,99 €![Bayesian Population Analysis Using WinBUGS]( "Bayesian Population Analysis Using WinBUGS")
Bayesian Population Analysis Using WinBUGS160,99 €![Trimmed Estimator]( "Trimmed Estimator")
Trimmed Estimator25,99 €
High Quality Content by WIKIPEDIA articles! In statistics, 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. One general result is that many standard estimators can be improved, in terms of mean squared error (MSE), by shrinking them towards zero (or any other fixed constant value).