Results 261 to 270 of about 2,638,357 (294)

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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Modified Ridge Regression Estimators

Communications in Statistics - Theory and Methods, 2013
Ridge regression is a variant of ordinary multiple linear regression whose goal is to circumvent the problem of predictors collinearity. It gives up the Ordinary Least Squares (OLS) estimator as a method for estimating the parameters [] of the multiple linear regression model [] .
G. Khalaf, Kristofer Månsson, Ghazi Shukur   +2 more
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On Some Ridge Regression Estimators: An Empirical Comparisons

Communications in Statistics - Simulation and Computation, 2009
In ridge regression analysis, the estimation of the ridge parameter k is an important problem. Many methods are available for estimating such a parameter. This article reviewed and proposed some estimators based on Kibria (2003) and Khalaf and Shukur (2005).
Gisela Muniz, B. M. Golam Kibria
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POISSON RIDGE REGRESSION ESTIMATORS

Advances and Applications in Statistics
This paper proposes a new Poisson ridge regression estimator using grid search. The new and known ridge estimators were then compared based on MSE criterion using Monte Carlo simulation. Different values of parameters were considered, such as sample size of greater than or equal to 10; correlation values of 0.85 to 0.99; and number of explanatory ...
Jerson S. Mohamad, Angelyn S. Delica, Shiela May M. Ledesma   +2 more
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Bayesian estimation of the shrinkage parameter in ridge regression

Communications in Statistics - Simulation and Computation, 2019
A common problem in the practice of regression analysis is multicollinearity. Its negative effects on the Least Squares estimator are well known.
Luis Firinguetti-Limone, Manuel Pereira-Barahona   +1 more
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On ecological regression and ridge estimation

Communications in Statistics - Simulation and Computation, 1995
This paper focuses on the development of an ecological regression approach for voter transition estimation, avoiding the arbitrary assumptions in Goodman's classical model of ecological regression (Goodman [1959]). In doing this, we further develop previous attempts made at the ridge regression approach, by applying a modified generalized ridge ...
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ROBUST RIDGE REGRESSION BASED ON AN M‐ESTIMATOR

Australian Journal of Statistics, 1991
SummaryConsider the linear regression model y=β01 +Xβ+ in the usual notation. It is argued that the class of ordinary ridge estimators obtained by shrinking the least squares estimator by the matrix (X1X + kI)‐1X'X is sensitive to outliers in the ^variable.
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Estimating Predictive Variances with Kernel Ridge Regression

2006
In many regression tasks, in addition to an accurate estimate of the conditional mean of the target distribution, an indication of the predictive uncertainty is also required. There are two principal sources of this uncertainty: the noise process contaminating the data and the uncertainty in estimating the model parameters based on a limited sample of ...
Cawley, G., Talbot, N., Chapelle, O.
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Minimax Linear Regression Estimators With Application to Ridge Regression

Technometrics, 1982
This article considers minimax linear estimation of β in the multiple linear-regression model Y = Xβ + ξ. Some results from European publications are referenced and summarized and some new results are given. These minimax estimators of β can also be classified as ridgeregression estimators with nonstochastic ridge parameters.
Lawrence Peele, Thomas P. Ryan
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A mixed estimator interpretation of ridge regression

Social Science Research, 1982
Abstract It is shown that a formal isomorphism between the ridge estimator and the homogeneous case of the Theil-Goldberger mixed estimator leads to a general interpretation of ridge regression as ordinary least squares estimation subject to a prior stochastic constraint that all slope coefficients in the model are zero. Users of ridge regression are
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