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

Autor:	Sukru Acitas, Birdal Şenoğlu
Rok vydání:	2020
Předmět:	Independent and identically distributed random variables Computational Mathematics Applied Mathematics Maximum likelihood Linear regression Phase (waves) Applied mathematics Estimator Point estimation Mathematics
Zdroj:	Journal of Computational and Applied Mathematics. 363:337-349
ISSN:	0377-0427
DOI:	10.1016/j.cam.2019.06.020
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::b5f148e1dcaba5c4e90f3991707535f8 https://doi.org/10.1016/j.cam.2019.06.020 Zobrazit plný text záznamu Full Text from ScienceDirect