scholarly journals A note on the relationships between multiple imputation, maximum likelihood and fully Bayesian methods for missing responses in linear regression models

2013 ◽  
Vol 6 (3) ◽  
pp. 315-324 ◽  
Author(s):  
Qingxia Chen ◽  
Joseph G. Ibrahim
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Multiple Imputation ◽  
Bayesian Methods ◽  
Regression Models ◽  
Linear Regression Models ◽  
Missing Responses ◽  
Fully Bayesian

scholarly journals QML Estimators in Linear Regression Models with Functional Coefficient Autoregressive Processes

2010 ◽  
Vol 2010 ◽  
pp. 1-30 ◽  
Author(s):  
Hongchang Hu
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Regression Models ◽  
Asymptotic Properties ◽  
Asymptotic Distributions ◽  
Autoregressive Processes ◽  
Linear Regression Models ◽  
Unknown Parameters ◽  
Quasi Maximum Likelihood ◽  
Functional Coefficient

This paper studies a linear regression model, whose errors are functional coefficient autoregressive processes. Firstly, the quasi-maximum likelihood (QML) estimators of some unknown parameters are given. Secondly, under general conditions, the asymptotic properties (existence, consistency, and asymptotic distributions) of the QML estimators are investigated. These results extend those of Maller (2003), White (1959), Brockwell and Davis (1987), and so on. Lastly, the validity and feasibility of the method are illuminated by a simulation example and a real example.

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