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

Abstract

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.

Prediction of Climate Change using SVM and Naïve Bayes Machine Learning Algorithms

“…In linear regression, which seeks to identify and forecast the low and high temperatures as a linear functional combinations of the features [12]. Since straight relapse can't be utilized with characterization information, this calculation didn't utilize the climate order of every day [15][16].Climatologically conditions need to be predicted to save the life of people which is a challenging problem. Machine learning techniques may be applied to forecast the extreme weather events.…”