Beta ridge regression estimators: simulation and application
MR Abonazel, IM Taha - Communications in Statistics-Simulation …, 2023 - Taylor & Francis
The beta regression model is commonly used when analyzing data that come in the form of
rates or percentages. However, a problem that may encounter when analyzing these kinds …
rates or percentages. However, a problem that may encounter when analyzing these kinds …
On some test statistics for testing the regression coefficients in presence of multicollinearity: a simulation study
S Perez-Melo, BMG Kibria - Stats, 2020 - mdpi.com
Ridge regression is a popular method to solve the multicollinearity problem for both linear
and non-linear regression models. This paper studied forty different ridge regression t-type …
and non-linear regression models. This paper studied forty different ridge regression t-type …
[HTML][HTML] A new robust ridge parameter estimator having no outlier and ensuring normality for linear regression model
In order to accurately estimate the regression coefficients in a multiple linear regression
model having multicollinearity, ridge regression is a well-liked biased estimation technique …
model having multicollinearity, ridge regression is a well-liked biased estimation technique …
Are most proposed ridge parameter estimators skewed and do they have any effect on MSE values?
Multicollinearity is a common problem in multiple regression that occurs whenever two or
more explanatory variables are highly correlated. When multicollinearity exists, the method …
more explanatory variables are highly correlated. When multicollinearity exists, the method …
Comparison of partial least squares with other prediction methods via generated data
The purpose of this study is to compare the Partial Least Squares (PLS), Ridge Regression
(RR) and Principal Components Regression (PCR) methods, used to fit regressors with …
(RR) and Principal Components Regression (PCR) methods, used to fit regressors with …
[PDF][PDF] Solving multicollinearity problem of gross domestic product using ridge regression method
This study is dedicated to solving multicollinearity problem for the general linear model by
using Ridge regression method. The basic formulation of this method and suggested forms …
using Ridge regression method. The basic formulation of this method and suggested forms …
A new robust ridge parameter estimator based on search method for linear regression model
ABSTRACT A large and wide variety of ridge parameter estimators proposed for linear
regression models exist in the literature. Actually proposing new ridge parameter estimator …
regression models exist in the literature. Actually proposing new ridge parameter estimator …
A comparison of different ridge parameters under both multicollinearity and heteroscedasticity
One of the major problems in fitting an appropriate linear regression model is
multicollinearity which occurs when regressors are highly correlated. To overcome this …
multicollinearity which occurs when regressors are highly correlated. To overcome this …
Optimum ridge regression parameter using R-squared of prediction as a criterion for regression analysis
A Irandoukht - Journal of Statistical Theory and Applications, 2021 - Springer
The presence of the multicollinearity problem in the predictor data causes the variance of the
ordinary linear regression coefficients to be increased so that the prediction power of the …
ordinary linear regression coefficients to be increased so that the prediction power of the …
How well do ridge parameter estimators proposed so far perform in terms of normality, outlier detection, and MSE criteria?
Ridge regression is a commonly used prediction method in cases of multicollinearity among
regressors in multiple linear regression model. In this study, the performances of 366 …
regressors in multiple linear regression model. In this study, the performances of 366 …