Add to ORKGWhen colinearity exists in a study of multiple linear regression, it will yield large estimated variances for the estimated coefficients in the model and it is then difficult to detect the “significant ” regression coefficients. The problems caused by colinearity ca
To date, little work investigating the effects of multicollinearity and developing assessment and es...
Multiple linear interferences are a fundamental obstacle in many standard models. This problem appea...
In our work, we explored multicollinearity problem from a complex point of view - from diagnostic me...
AbstractThe problem of estimating the common regression coefficients is addressed in this paper for ...
Biased regression is an alternative to ordinary least squares (OLS) regression, espe-cially when exp...
AbstractBiased regression is an alternative to ordinary least squares (OLS) regression, especially w...
In multiple linear regression, the ordinary least squares estimator is very sensitive to the presenc...
The generalized linear model (Nelder & Wedderburn, 1972) has become an elegant and practical opt...
In time series regression modelling, first-order autocorrelated errors are often a problem. When the...
In multiple linear regression analysis, linear dependencies in the regressor variables lead to ill-...
Investigators that seek to employ regression analysis usually encounter the problem of multicollinea...
AbstractPLS initially creates uncorrelated latent variables which are linear combinations of the ori...
VVe investigate a generalized linear model fbr dimensionality reduction of binary data. The model ...
Abstract: Linear Discriminant Analysis leads to unstable models and poor predictions in the presence...
[[abstract]]Estimation of regression coefficients in a linear regression model is essential not only...
To date, little work investigating the effects of multicollinearity and developing assessment and es...
Multiple linear interferences are a fundamental obstacle in many standard models. This problem appea...
In our work, we explored multicollinearity problem from a complex point of view - from diagnostic me...
AbstractThe problem of estimating the common regression coefficients is addressed in this paper for ...
Biased regression is an alternative to ordinary least squares (OLS) regression, espe-cially when exp...
AbstractBiased regression is an alternative to ordinary least squares (OLS) regression, especially w...
In multiple linear regression, the ordinary least squares estimator is very sensitive to the presenc...
The generalized linear model (Nelder & Wedderburn, 1972) has become an elegant and practical opt...
In time series regression modelling, first-order autocorrelated errors are often a problem. When the...
In multiple linear regression analysis, linear dependencies in the regressor variables lead to ill-...
Investigators that seek to employ regression analysis usually encounter the problem of multicollinea...
AbstractPLS initially creates uncorrelated latent variables which are linear combinations of the ori...
VVe investigate a generalized linear model fbr dimensionality reduction of binary data. The model ...
Abstract: Linear Discriminant Analysis leads to unstable models and poor predictions in the presence...
[[abstract]]Estimation of regression coefficients in a linear regression model is essential not only...
To date, little work investigating the effects of multicollinearity and developing assessment and es...
Multiple linear interferences are a fundamental obstacle in many standard models. This problem appea...
In our work, we explored multicollinearity problem from a complex point of view - from diagnostic me...