کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4949353 | 1440047 | 2017 | 19 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Estimation of selected parameters
ترجمه فارسی عنوان
برآورد پارامترهای انتخاب شده
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
چکیده انگلیسی
Modern statistical problems often involve selection of populations (or genes for example) using the observations. After selecting the populations, it is important to estimate the corresponding parameters. These quantities are called the selected parameters. Using traditional estimators, such as maximum likelihood (ML) estimator, which ignores the selection can result in a large bias. It is, however, known that the Bayes estimator that ignores the selection still works well under the assumed prior distribution. But, when the prior distribution used to derive the Bayes estimator is very different from the “true” prior, the Bayes estimator can fail. The paper aims to construct estimators for the selected parameters which are robust to prior distributions. A generalization of the multiple-shrinkage Stein type estimator proposed by George (1986a, 1986b) is proposed and is shown to have a small selection bias for estimating the selected means and have an attractive small expected mean squared error. With respect to these two criteria, the proposed estimator is generally better than ML estimator, Lindley-James-Stein (LJS) estimator and Efron-Tweedie (Efron, 2011) estimator.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 109, May 2017, Pages 45-63
Journal: Computational Statistics & Data Analysis - Volume 109, May 2017, Pages 45-63
نویسندگان
Jia-Chiun Pan, Yufen Huang, J.T. Gene Hwang,