On the least trimmed squares estimators for JS']JS circular regression model

被引：2

作者：

Alshqaq, Shokrya Saleh ^{[1
]}

机构：

[1] Jazan Univ, Coll Sci, Dept Math, Jizan, Saudi Arabia

来源：

KUWAIT JOURNAL OF SCIENCE | 2021年 / 48卷 / 03期

关键词：

Breakdown point; circular regression; LTS estimation; outliers; robust estimation; LIKELIHOOD;

D O I：

10.48129/kjs.v48i3.10004

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The least trimmed squares (LTS) estimation has been successfully used in the robust linear regression models. This article extends the LTS estimation to the Jammalamadaka and Sarma (JS) circular regression model. The robustness of the proposed estimator is studied and the used algorithm for computation is discussed. Simulation studied, and real data show that the proposed robust circular estimator effectively fits JS circular models in the presence of vertical outliers and leverage points.

引用

页数：13

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