[HTML][HTML] Condition-index based new ridge regression estimator for linear regression model with multicollinearity
Ridge regression is employed to estimate the regression parameters while circumventing
the multicollinearity among independent variables. The ridge parameter plays a vital role as …
the multicollinearity among independent variables. The ridge parameter plays a vital role as …
[PDF][PDF] An empirical assessment on the transportation sustainability indicators and their impact on economic productivity
AA Kutty, Z Yetiskin, MM Abraham… - Proceedings of the …, 2020 - researchgate.net
Sustainable transportation has been traditionally acknowledged as an accelerating factor in
achieving economic productivity. This paper attempts to frame a methodology to study the …
achieving economic productivity. This paper attempts to frame a methodology to study the …
Modified Liu estimators in the linear regression model: An application to Tobacco data
Background The problem of multicollinearity in multiple linear regression models arises
when the predictor variables are correlated among each other. The variance of the ordinary …
when the predictor variables are correlated among each other. The variance of the ordinary …
Combating the multicollinearity in Bell regression model: Simulation and application
GA Shewa, FI Ugwuowo - Journal of the Nigerian Society of …, 2022 - journal.nsps.org.ng
Poisson regression model has been popularly used to model count data. However, over-
dispersion is a threat to the performance of the Poisson regression model. The Bell …
dispersion is a threat to the performance of the Poisson regression model. The Bell …
Weighted penalized m-estimators in robust ridge regression: An application to gasoline consumption data
The OLS and ridge regression (RR) estimators are adversely affected, when the problem of
multicollinearity and y-direction outliers occur together. The robust ridge regression with …
multicollinearity and y-direction outliers occur together. The robust ridge regression with …
On the performance of two-parameter ridge estimators for handling multicollinearity problem in linear regression: Simulation and application
The inability of ordinary least square estimators against multicollinearity has paved the way
for the development of various ridge-type estimators, which are recently classified as one …
for the development of various ridge-type estimators, which are recently classified as one …
Performance of some new ridge parameters in two-parameter ridge regression model
Two-parameter ridge regression is a widely used method in the last two decades to
circumvent the problem of multicollinearity. Ridge parameter k plays an important role in …
circumvent the problem of multicollinearity. Ridge parameter k plays an important role in …
New quantile based ridge M-estimator for linear regression models with multicollinearity and outliers
The ordinary least squares and ridge regression estimators in a multiple linear regression
model with multicollinearity and y-direction outliers lead to unfavorable results. In order to …
model with multicollinearity and y-direction outliers lead to unfavorable results. In order to …
Modified Robust Ridge M‐Estimators in Two‐Parameter Ridge Regression Model
S Yasin, S Salem, H Ayed, S Kamal… - Mathematical …, 2021 - Wiley Online Library
The methods of two‐parameter ridge and ordinary ridge regression are very sensitive to the
presence of the joint problem of multicollinearity and outliers in the y‐direction. To overcome …
presence of the joint problem of multicollinearity and outliers in the y‐direction. To overcome …
Modified robust ridge M-estimators for linear regression models: an application to tobacco data
The ordinary least squared and ridge regression estimators in a linear regression model are
sensitive to outliers in the y-variable. In such situations, ridge M-estimators are widely used …
sensitive to outliers in the y-variable. In such situations, ridge M-estimators are widely used …