Empirical likelihood ratio tests for the linear regression model with inequality constraints

2016 ◽  
Vol 45 (20) ◽  
pp. 5933-5945
Author(s):  
Li Chen
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Likelihood Ratio ◽  
Empirical Likelihood ◽  
Linear Regression Model ◽  
Inequality Constraints ◽  
Likelihood Ratio Tests ◽  
Empirical Likelihood Ratio

scholarly journals A Discourse on Modified Likelihood Ratio (LR), Wald and Lagrange Multipliers (LM) Tests for Testing General Linear Hypothesis in Stochastic Linear Regression Model

2018 ◽  
Vol 7 (4.10) ◽  
pp. 536
Author(s):  
C. Narayana ◽  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
J. Ravi sankar ◽  
P. Balasiddamuni
Keyword(s):  
Maximum Likelihood ◽  
Linear Regression ◽  
Regression Model ◽  
Likelihood Ratio ◽  
Linear Regression Model ◽  
General Linear ◽  
Linear Hypothesis ◽  
General Linear Hypothesis ◽  
Modified Likelihood ◽  
Studentized Residuals

In this research paper various new advanced inferential tools namely modified likelihood ratio (LR), Ward and Lagrange Multiplier test statistics have been proposed for testing general linear hypothesis in stochastic linear regression model. In this process internally studentized residuals have been used. This research study has brought out some new advance tools for analysing inferential aspects of stochastic linear regression models by using internally studentized residuals. Miguel Fonseca et.al [1] developed statistical inference in linear models dealing with the theory of maximum likelihood estimates and likelihood ratio tests under some linear inequality restrictions on the regression coefficients. Tim Coelli [2] used Monte carlo experimentation to investigate the finite sample properties of maximum likelihood (ML) and correct ordinary least squares (COLS) estimators of the half –normal stochastic frontier production function. In 2011, p. Bala siddamuni et.al [3] have developed advanced tools for mathematical and stochastic modelling.  

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