scholarly journals A Treatise on Testing General Linear Hypothesis in Stochastic Linear Regression Model

2018 ◽  
Vol 7 (4.10) ◽  
pp. 539
Author(s):  
C. Narayana ◽  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
J. Ravi sankar ◽  
P. Balasiddamuni
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
General Linear ◽  
Linear Hypothesis ◽  
Test Statistics ◽  
Test Statistic ◽  
General Linear Hypothesis ◽  
Testing Linear ◽  
Studentized Residuals

The main objective of this research article is to propose test statistics for testing general linear hypothesis about parameters in stochastics linear regression model using studentized residuals, RLS estimates and unrestricted internally studentized residuals. In 1998, M. Celia Rodriguez -Campos et.al [1] introduced a new test statistics to test the hypothesis of a generalized linear model in a regression context with random design. Li Cai et.al [2] provide a new test statistic for testing linear hypothesis in an OLS regression model that not assume homoscedasticity. P. Balasiddamuni et.al [3] proposed some advanced tools for mathematical and stochastical modelling.  

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.  

Cadmium-hazard mapping using a general linear regression model (Irr-Cad) for rapid risk assessment

2008 ◽  
Vol 31 (1) ◽  
pp. 71-79 ◽  
Author(s):  
Robert W. Simmons ◽  
Andrew D. Noble ◽  
P. Pongsakul ◽  
O. Sukreeyapongse ◽  
N. Chinabut
Keyword(s):  
Risk Assessment ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
General Linear ◽  
Hazard Mapping ◽  
General Linear Regression Model
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