Robust estimation in joint mean–covariance regression model for longitudinal data

2012 ◽  
Vol 65 (4) ◽  
pp. 617-638 ◽  
Author(s):  
Xueying Zheng ◽  
Wing Kam Fung ◽  
Zhongyi Zhu
Keyword(s):  
Longitudinal Data ◽  
Regression Model ◽  
Robust Estimation

Adaptive robust estimation in joint mean–covariance regression model for bivariate longitudinal data

Statistics ◽  
2017 ◽  
Vol 52 (1) ◽  
pp. 64-83 ◽  
Author(s):  
Jing Lv ◽  
Chaohui Guo ◽  
Tingting Li ◽  
Yuanyuan Hao ◽  
Xiaolin Pan
Keyword(s):  
Longitudinal Data ◽  
Regression Model ◽  
Robust Estimation

scholarly journals Robust Estimation of Generalized Estimating Equation when Data Contain Outliers

INSIST ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. 1 ◽  
Author(s):  
Khoirin Nisa ◽  
Netti Herawati
Keyword(s):  
Longitudinal Data ◽  
Regression Model ◽  
Robust Estimation ◽  
Simulation Study ◽  
Estimating Equation ◽  
Least Square ◽  
Generalized Estimating Equation ◽  
Data Regression ◽  
Generalized Estimating

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.

scholarly journals A robust estimation for the extended t-process regression model

2020 ◽  
Vol 157 ◽  
pp. 108626
Author(s):  
Zhanfeng Wang ◽  
Kai Li ◽  
Jian Qing Shi
Keyword(s):  
Regression Model ◽  
Robust Estimation
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