Open AccessOn the least trimmed squares estimators for JS circular regression model
Author(s) -
Shokrya S. Alshqaq
Publication year - 2021
Publication title -
maǧallaẗ al-kuwayt li-l-ʿulūm
Language(s) - English
Resource type - Journals
eISSN - 2307-4116
pISSN - 2307-4108
DOI - 10.48129/kjs.v48i3.10004
Subject(s) - least trimmed squares , robust regression , estimator , outlier , leverage (statistics) , mathematics , robust statistics , total least squares , generalized least squares , regression analysis , computation , robustness (evolution) , statistics , least squares function approximation , linear regression , regression , algorithm , biochemistry , chemistry , gene
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.