翻訳と辞書 Words near each other ・ Shrink Dust ・ Shrink Rap ・ Shrink Rap (TV series) ・ Shrink ray ・ Shrink to survive ・ Shrink tunnel ・ Shrink wrap ・ Shrink wrap (disambiguation) ・ Shrink wrap contract ・ Shrink Yourself ・ Shrink-fitting ・ Shrinkage ・ Shrinkage (accounting) ・ Shrinkage (fabric) ・ Shrinkage (statistics) ・ Shrinkage estimator ・ Shrinkflation ・ Shrinking cities ・ Shrinking cities in the United States ・ Shrinking generator ・ Shrinking Man ・ Shrinking space ・ Shrinking the Blob ・ Shrinking the footprint ・ Shrinking Violet ・ Shrinking Violet (album) ・ Shrinkwrapped ・ Shrinkwrapped (album) ・ Shrinky Dinks ・ Shrink–swell capacity Dictionary Lists mini英和辞書 mini和英辞書 Webster 1913 Latin-English FOLDOC Wikipedia English ウィキペディア

翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Shrinkage estimator ： ウィキペディア英語版 Shrinkage estimator 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. ==Examples== 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). Assume that the expected value of the raw estimate is not zero and consider other estimators obtained by multiplying the raw estimate by a certain parameter. A value for this parameter can be specified so as to minimize the MSE of the new estimate. For this value of the parameter, the new estimate will have a smaller MSE than the raw one. Thus it has been improved. An effect here may be to convert an unbiased raw estimate to an improved biased one. A well-known example arises in the estimation of the population variance by sample variance. For a sample size of ''n'', the use of a divisor ''n'' − 1 in the usual formula (Bessel's correction) gives an unbiased estimator, while other divisors have lower MSE, at the expense of bias. The optimal choice of divisor (weighting of shrinkage) depends on the excess kurtosis of the population, as discussed at mean squared error: variance, but one can always do better (in terms of MSE) than the unbiased estimator; for the normal distribution a divisor of ''n'' + 1 gives one which has the minimum mean square error. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Shrinkage estimator」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.