Results 251 to 260 of about 33,887 (293)

Error covariance matrix estimation using ridge estimator

Statistics & Probability Letters, 2013
Abstract This article considers sparse covariance matrix estimation of high dimension. In contrast to the existing methods which are based on the residual estimation from least squares estimator, we utilize residuals from ridge estimator with the adaptive thresholding technique to estimate the error covariance matrix in high dimensional factor model.
June Luo, K.B. Kulasekera
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Ridge estimator with correlated errors and two-stage ridge estimator under inequality restrictions

Communications in Statistics - Theory and Methods, 2016
AbstractLiew (1976a) introduced generalized inequality constrained least squares (GICLS) estimator and inequality constrained two-stage and three-stage least squares estimators by reducing primal–dual relation to problem of Dantzig and Cottle (1967), Cottle and Dantzig (1974) and solving with Lemke (1962) algorithm.
Toker S., Kaçıranlar S.
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Unbiased ridge estimation with prior information and ridge trace

Communications in Statistics - Theory and Methods, 1995
A 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, null Chun Jin, R. C. Hanumara   +2 more
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PMC Theorems on PCR–Ridge Class Estimators

Journal of Statistical Theory and Practice, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Yuanhan, Keating, Jerome P., Balakrishnan, Narayanaswamy   +2 more
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A Tobit Ridge Regression Estimator

Communications in Statistics - Theory and Methods, 2013
This 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, K. Månsson, P. Sjölander, G. Shukur   +3 more
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Ridge estimation in logistic regression

Communications in Statistics - Simulation and Computation, 1988
The 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, 1979
Abstract 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, 2017
ABSTRACTIt 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, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Toker S., Üstündag Şiray G., Kaçiranlar S.   +2 more
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A Comparison of Ridge Estimators

Technometrics, 1978
Least 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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