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Comparisons of the Unbiased Ridge Estimation to the Other Estimations
Communications in Statistics - Theory and Methods, 2007In the presence of multicollinearity, ordinary least squares (OLS) estimation is inadequate. Alternative estimation techniques were proposed. One of which is unbiased ridge regression (URR) estimator given by Crouse et al. (1995). In this article, we introduced the URR estimator in two different ways by following Farebrother (1984) and Troskie et al ...
Özkale M.R., Kaçiranlar S.
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Statistics & Probability Letters, 2003
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A Comparison of Ridge Estimators
Technometrics, 1978Least squares estimates of the parameters in the usual linear regression model are likely to be too large in absolute value and possibly of the wrong sign when the vectors of explanatory variables are multicollinear. Hoer1 and Kennard have demonstrated that these undesirable effects of multicollinearity can be reduced by using “ridge” estimates in ...
Dean W. Wichern, Gilbert A. Churchill
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Communications in Statistics - Theory and Methods, 1984
It is found that multicollinearity among the independent variables in logistic regression inflates the variances of the maximum likelihood estimator. A Ridge type estimator is proposed that will have smaller total mean squared error than the maximum likelihood estimator under certain conditions.
R.L. Schaefer, L.D. Roi, R.A. Wolfe
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It is found that multicollinearity among the independent variables in logistic regression inflates the variances of the maximum likelihood estimator. A Ridge type estimator is proposed that will have smaller total mean squared error than the maximum likelihood estimator under certain conditions.
R.L. Schaefer, L.D. Roi, R.A. Wolfe
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A genetic algorithm for the estimation of ridges in fingerprints
IEEE Transactions on Image Processing, 1999A genetic algorithm is developed to find the ridges in paper fingerprints. It is based on the fact that the ridges of the fingerprints are parallel. When scanning the fingerprint, line by line, the ideal noise-free gray level distribution should yield lines of black and white. The widths of these lines are not constant.
Ahmed S. Abutaleb, Mohamed S. Kamel
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Shrinkage Ridge Estimators in Linear Regression
Communications in Statistics - Simulation and Computation, 2013The problem of estimation of the regression coefficients in a multiple regression model (MRM) is considered under multicollinearity situation. Further it is suspected that the regression coefficients may be restricted to a subspace. In this approach, we present the estimators of the regression coefficients combining the idea of preliminary test ...
Mohammad Arashi +2 more
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Unbiased ridge estimation with prior information and ridge trace
Communications in Statistics - Theory and Methods, 1995A procedure is illustrated to incorporate prior information in the ridge regression model. Unbiased ridge estimators with prior information are defined and a robust estimate of the ridge parameter k is proposed.
Robert H. Crouse +2 more
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A ‘conservative’ ridge estimator
Economics Letters, 1979Abstract In this paper an alternative to the Ordinary Ridge Estimator (ORE) introduced by Hoerl and Kennard (1970) is proposed. This estimator is called a ‘Conservative’ Ridge Estimator (CRE), because it puts a heavier weight on the unbiasedness and a smaller weight on the statistical stability of the ‘unstable’ estimation components than the ORE ...
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A new biased estimator based on ridge estimation
Statistical Papers, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sakallioglu S., Kaçiranlar S.
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Communications in Statistics - Theory and Methods, 1998
Swindel (1976) introduced a modified ridge regression estimator based on prior information. Sarkar (1992) suggested a new estimator by combining in a particular way the two approaches followed in obtaining the restricted ieast squares and ordinary ndge regression estimators.
Kaçiranlar S. +2 more
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Swindel (1976) introduced a modified ridge regression estimator based on prior information. Sarkar (1992) suggested a new estimator by combining in a particular way the two approaches followed in obtaining the restricted ieast squares and ordinary ndge regression estimators.
Kaçiranlar S. +2 more
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