Results 141 to 150 of about 548,663 (198)

Ridge Estimators in Logistic Regression

Applied Statistics, 1992
Summary: In this paper it is shown how ridge estimators can be used in logistic regression to improve the parameter estimates and to diminish the error made by further predictions. Different ways to choose the unknown ridge parameter are discussed. The main attention focuses on ridge parameters obtained by cross-validation.
le Cessie, S., van Houwelingen, J. C.
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A Poisson ridge regression estimator

Economic Modelling, 2011
The standard statistical method for analyzing count data is the Poisson regression model, which is usually estimated using maximum likelihood (ML) method.
Månsson, Kristofer, Shukur, Ghazi
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Fingerprint Ridge Distance Estimation Based on Ridge Search

2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, 2009
Fingerprint ridge distance is a very useful parameter in AFIS (automatic fingerprint identification system), it can be used for fingerprint enhancement, identification or classification. In this paper, a new ridge distance estimation method based on the ridge search is presented. Firstly, we use the IPL(initial points line) to select the initial-points
Liming Zhang 0006, Yilong Yin, Dong Yang
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Comparisons of the Unbiased Ridge Estimation to the Other Estimations

Communications in Statistics - Theory and Methods, 2007
In 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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Restricted ridge estimation

Statistics & Probability Letters, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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A modified ridge estimator in Cox regression model

Communications in statistics. Simulation and computation
The most used approach for survival data is the Cox proportional hazards regression model. Multicollinearity, however, is known to have a detrimental impact on the variance of maximum likelihood estimator of the Cox regression coefficients.
Z. Algamal
semanticscholar   +1 more source

A ridge logistic estimator

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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Modified ridge estimator in the Bell regression model

Journal of Inverse and Ill-Posed Problems
The Bell regression model (BRM), a member of the generalized linear models (GLMs), can be used when the dependent variable consists of overdispersed count data.
Y. M. Bulut, A. Lukman, Melike Işılar, E. Adewuyi, Z. Algamal   +4 more
semanticscholar   +1 more source

Almost unbiased ridge estimator in the gamma regression model

Communications in statistics. Simulation and computation, 2020
This article introduces the almost unbiased gamma ridge regression estimator (AUGRRE) estimator based on the gamma ridge regression estimator (GRRE). Furthermore, some shrinkage parameters are proposed for the AUGRRE.
M. Amin, Muhammad Qasim, Ahad Yasin, M. Amanullah   +3 more
semanticscholar   +1 more source