INSIST (International Series on Interdisciplinary Research)
Vol 2, No 1 (2017)

Robust Estimation of Generalized Estimating Equation when Data Contain Outliers

Khoirin Nisa (Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Lampung, Jalan Prof. Soemantri Brojonegoro No. 1, Bandar Lampung, Indonesia.)
Netti Herawati (Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Lampung, Jalan Prof. Soemantri Brojonegoro No. 1, Bandar Lampung, Indonesia.)

Article Info

Publish Date
13 Jul 2017

Abstract

Abstract—In this paper, a robust procedure for estimating parameters of regression model when generalized estimating equation (GEE) applied to longitudinal data that contains outliers is proposed. The method is called ‘iteratively reweighted least trimmed square’ (IRLTS) which is a combination of the iteratively reweighted least square (IRLS) and least trimmed square (LTS) methods. To assess the proposed method a simulation study was conducted and the result shows that the method is robust against outliers.Keywords—GEE, IRLS, LTS, longitudinal data, regression model.

Copyrights © 2017




Citation Download
RIS
EndNote, Reference Manager, ProCite
BibTex
Latex, Jabref
Full PDF
94.379 KB
Original Source
Download Original
Google Scholar
Check in Google Scholar
Journal Info
INSIST (International Series on Interdisciplinary Research)
Website

Abbrev

ojs

Publisher

Universitas Lampung

Subject

Other

Description

INSIST is an International online journal which publishes innovative research papers and critical reviews in the field of engineering and interdisciplinary science researches. It focuses on but not limited to Electrical and Telecommunication, Mechanical Engineering, Chemical and Environmental ...