scholarly journals Least Absolute Deviation Estimate for Functional Coefficient Partially Linear Regression Models

2012 ◽  
Vol 2012 ◽  
pp. 1-20
Author(s):  
Yanqin Feng ◽  
Guoxin Zuo ◽  
Li Liu
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Least Absolute Deviation ◽  
Linear Regression Models ◽  
Absolute Deviation ◽  
Partially Linear ◽  
Deviation Estimate ◽  
Functional Coefficient

Test for Heteroscedasticity in Partially Linear Regression Models

2019 ◽  
Vol 32 (4) ◽  
pp. 1194-1210 ◽  
Author(s):  
Waled Khaled ◽  
Jinguan Lin ◽  
Zhongcheng Han ◽  
Yanyong Zhao ◽  
Hongxia Hao
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Partially Linear

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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