Ridge Regression, Liu and Related Estimators
A special issue of Stats (ISSN 2571-905X).
Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3450
Special Issue Editor
Special Issue Information
Dear Colleagues,
Multiple linear regression models play an important role in statistical inference and are used extensively in business, industry, physical, and social sciences. In this model, it is general practice to assume that explanatory variables are independent. However, in practice, there may be strong or near-strong linear relationships among the explanatory variables. In that case, independence assumptions are no longer valid, which causes the problem of multicollinearity. In the presence of multicollinearity, it is very difficult to estimate the unique effects of individual variables in the regression equation. Moreover, the estimated regression coefficients will have large sampling variances which affect both valid inference and prediction. Multicollinearity is a common problem in the field of engineering, economics, social sciences, and physical sciences. Ridge regression is one of the most useful techniques to solve the problem of multicollinearity. The performance of ridge regression (RR) estimators depends on the value of ridge parameter k. Liu (1993) suggested another shrinkage estimator where the parameter has the benefit of being a linear function of the shrinkage parameter d. Due to this advantage over ridge regression, the Liu estimator has been applied and studied by many researchers.
This Special Issue will focus on the estimation of Ridge parameter k and shrinkage estimator d, as well as preliminary and shrinkage estimation for the ridge regression and Liu regression models.
Special issue topics include (but are not limited to):
Estimation of the ridge parameter k for both linear and nonlinear regression models
Estimation of the ridge parameter k for restricted linear and nonlinear regression models
Statistical inference (confidence interval and hypothesis testing) about the ridge parameter k
Pre-test and shrinkage estimation for Ridge regression model
Estimation of the shrinkage parameter d for both linear and nonlinear regression models
Estimation of the shrinkage parameter d for restricted linear and nonlinear regression models
Statistical Inference (confidence interval and hypothesis testing) about the shrinkage parameter d
Pre-test and shrinkage estimation for Liu regression model
Prof. Dr. B. M. Golam Kibria
Guest Editor
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Keywords
- linear model
- ridge regression
- Liu estimator
- shrinkage estimation
- MSE
- simulation study
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