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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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PMC Theorems on PCR–Ridge Class Estimators
Journal of Statistical Theory and Practice, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Yuanhan +2 more
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A Tobit Ridge Regression Estimator
Communications in Statistics - Theory and Methods, 2013This article analyzes the effects of multicollienarity on the maximum likelihood (ML) estimator for the Tobit regression model. Furthermore, a ridge regression (RR) estimator is proposed since the mean squared error (MSE) of ML becomes inflated when the regressors are collinear. To investigate the performance of the traditional ML and the RR approaches
G. Khalaf +3 more
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Ridge estimation in logistic regression
Communications in Statistics - Simulation and Computation, 1988The variance of the Maximum Likelihood Estimator (MLE) of the slope parameter in a logistic regression model becomes large as the degree of collinearity among the explanatory variables increases. In a Monte Carlo study, we observed that a ridge type estimator is at least as good as, and often much better than, the MLE in terms of Total and Prediction ...
A. H. Lee, M. J. Silvapulle
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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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Improved robust ridge M-estimation
Journal of Statistical Computation and Simulation, 2017ABSTRACTIt is developed that non-sample prior information about regression vector-parameter, usually in the form of constraints, improves the risk performance of the ordinary least squares estimator (OLSE) when it is shrunken. However, in practice, it may happen that both multicollinearity and outliers exist simultaneously in the data.
M. Norouzirad, M. Arashi, S. E. Ahmed
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Inequality constrained ridge regression estimator
Statistics & Probability Letters, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Toker S. +2 more
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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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On some improved ridge estimators
Statistische Hefte, 1987zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Singh, Balvir, Chaubey, Yogendra P.
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Ridge Autoregression Estimation: LS Method
Communications in Statistics - Theory and Methods, 2015In an AR (p)-model, least-squares estimation of the parameters is considered when it is suspected that the parameters may belong to a linear subspace and the estimated covariance matrix is ill-conditioned. Accordingly, we define five estimators and study their properties in an asymptotic setup to discover dominance properties based on asymptotic ...
A. K. MD. Ehsanes Saleh, Amal F. Ghania
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