Robust estimators for circular regression models

The problem of robust estimation in circular regression models has not been studied well. This paper considers the JS circular regression model due to its interesting properties and its sensitivity for existence and detection of outliers. We extend the robust estimators such as M-estimation, least-trimmed squares (LTS), and least-median squares (LMS) estimators, which have been successfully used in the linear regression models, to the JS circular regression model.