Testing for outliers in nonlinear longitudinal data models based on M-estimation
Year: 2017
Journal of Information and Computing Science, Vol. 12 (2017), Iss. 2 : pp. 107–112
Abstract
In this paper we propose and analyze nonlinear mixed-effects models for longitudinal data, obtaining robust maximum likelihood estimates for the parameters by introducing Huber’s function in the log-likelihood function. Furthermore, the test for outliers in the model based on robust estimation is investigated through generalized Cook’s distance. The obtained results are illustrated by plasma concentrations data presented in Davidian and Giltiman, which was analyzed under the non-robust situation.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2024-JICS-22486
Journal of Information and Computing Science, Vol. 12 (2017), Iss. 2 : pp. 107–112
Published online: 2017-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 6