Sufficient jackknife-after-bootstrap method for detection of influential observations in linear regression models

被引：13

作者：

Beyaztas, Ufuk ^{[1
]}

Alin, Aylin ^{[1
]}

机构：

[1] Dokuz Eylul Univ, Dept Stat, Izmir, Turkey

来源：

STATISTICAL PAPERS | 2014年 / 55卷 / 04期

关键词：

Sufficient bootstrap; Jacknife; Bootstrap; Influential observation; Regression diagnostics;

D O I：

10.1007/s00362-013-0548-4

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this study, we adapt sufficient bootstrap into the jackknife-after-bootstrap (JaB) algorithm. The performances of the sufficient and conventional JaB methods have been compared for detecting influential observations in linear regression. Comparison is based on two real-world examples and an extensive designed simulation study. Design includes different sample sizes and various modeling scenarios. The results reveal that proposed method is a good competitor for conventional JaB method with less standard error and amount of computation.

引用

页码：1001 / 1018

页数：18

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