Robust change point estimation in two-phase linear regression models: An application to metabolic pathway data

Abstract

In this study, we develop robust versions of the change point estimation methods given by Hudson (1966) and Muggeo (2003) in the two-phase linear regression model. We use a modified maximum likelihood (MML) methodology originated by Tiku (1967, 1968) when the error terms of a two-phase linear regression model are independently and identically distributed as long-tailed symmetric. Proposed estimators are shown to be more efficient and robust using the Monte-Carlo simulation. Julious's (Julious, 2001) metabolic pathway data is analyzed in the application part of the study. It is shown that for this data using a LS estimator is inappropriate since there is an outlying observation. We therefore use proposed robust estimators instead of LS estimators and obtain more reliable results. (C) 2019 Elsevier B.V. All rights reserved.