Wan Fung Lee
Biographic Data
| ID | 4270702 |
|---|---|
| NAME | Wan Fung Lee |
| GIVEN NAMES | Wan Fung |
| FAMILY NAME | Lee |
| SIGNATURE | LEE W F |
| AFFILIATIONS | Memorial University of Newfoundland |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 1 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 1983 |
| LATEST PUBLICATION YEAR | 1984 |
| H-INDEX | 1 |
The R2 Ridge Trace in 2SLS Regression Estimation
Although the two-stage least squares (2SLS) estimator has several desirable properties, and thus is a preferred method of equation estimation, it is nevertheless extremely sensitive to multicollinearity. In recent years, ridge regression (RR) has become a popular approach for coping with multicollinearity because it usually generates smaller estimator variance than least squares methods. In theory, then, two-stage ridge regression (2SRR) should a…
Normalization Ridge Regression in Practice
Both ordinary least squares (OLS) and two-stage least squares (2SLS) regression methods are sensitive to multicollinearity. The standard statistical solution to the multicollinearity problem is to use one of a family of biased, variance-reduced estimation methods collectively known as ridge regression (RR). In the presence of multicollinearity, RR is usually more efficient than OLS; thus, in theory, two-stage ridge regression (2SRR) should be abl…
Normalization Ridge Regression in Practice
Both ordinary least squares (OLS) and two-stage least squares (2SLS) regression methods are sensitive to multicollinearity. The standard statistical solution to the multicollinearity problem is to use one of a family of biased, variance-reduced estimation methods collectively known as ridge regression (RR). In the presence of multicollinearity, RR is usually more efficient than OLS; thus, in theory, two-stage ridge regression (2SRR) should be abl…
Normalization Ridge Regression in Practice
Both ordinary least squares (OLS) and two-stage least squares (2SLS) regression methods are sensitive to multicollinearity. The standard statistical solution to the multicollinearity problem is to use one of a family of biased, variance-reduced estimation methods collectively known as ridge regression (RR). In the presence of multicollinearity, RR is usually more efficient than OLS; thus, in theory, two-stage ridge regression (2SRR) should be abl…
The R2 Ridge Trace in 2SLS Regression Estimation
Although the two-stage least squares (2SLS) estimator has several desirable properties, and thus is a preferred method of equation estimation, it is nevertheless extremely sensitive to multicollinearity. In recent years, ridge regression (RR) has become a popular approach for coping with multicollinearity because it usually generates smaller estimator variance than least squares methods. In theory, then, two-stage ridge regression (2SRR) should a…
Advanced Statistical Methods and Models (2 works) · Econometrics (2 works) · Mathematics (2 works) · Multicollinearity (2 works) · Regression (2 works) · Regression analysis (2 works) · Spectroscopy and Chemometric Analyses (2 works) · Statistics (2 works) · Variance inflation factor (2 works) · Advanced Statistical Process Monitoring (1 works)