Results 251 to 260 of about 25,104 (288)

Mean squared error comparisons of the modified ridge regression estimator and iiie restricted ridge regression estimator

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., Sakallioglu S., Akdeniz F.   +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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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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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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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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Restricted ridge estimator in the logistic regression model

Communications in Statistics - Simulation and Computation, 2016
ABSTRACTIt is known that when the multicollinearity exists in the logistic regression model, variance of maximum likelihood estimator is unstable. As a remedy, Schaefer et al. presented a ridge estimator in the logistic regression model. Making use of the ridge estimator, when some linear restrictions are also present, we introduce a restricted ridge ...
Yasin Asar, Mohammad Arashi, Jibo Wu
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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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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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