Results 211 to 220 of about 123,313 (259)
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Inverse Gaussian Liu-type estimator
Communications in Statistics - Simulation and Computation, 2021The inverse Gaussian regression (IGR) model parameters are generally estimated using the maximum likelihood (ML) estimation method.
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Liu-Type Multinomial Logistic Estimator
Sankhya B, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mohamed R. Abonazel, Rasha A. Farghali
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Communications in Statistics - Simulation and Computation, 2013
It is known that multicollinearity inflates the variance of the maximum likelihood estimator in logistic regression. Especially, if the primary interest is in the coefficients, the impact of collinearity can be very serious. To deal with collinearity, a ridge estimator was proposed by Schaefer et al. The primary interest of this article is to introduce
Deniz Inan, Birsen E. Erdogan
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It is known that multicollinearity inflates the variance of the maximum likelihood estimator in logistic regression. Especially, if the primary interest is in the coefficients, the impact of collinearity can be very serious. To deal with collinearity, a ridge estimator was proposed by Schaefer et al. The primary interest of this article is to introduce
Deniz Inan, Birsen E. Erdogan
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Liu-type estimator in semiparametric regression models
Journal of Statistical Computation and Simulation, 2010In this paper, we introduced a Liu-type estimator for the vector of parameters β in a semiparametric regression model. We also obtained the semiparametric restricted Liu-type estimator for the parametric component in a semiparametric regression model. The ideas in the paper are illustrated in a real data example and in a Monte Carlo simulation study.
Akdeniz F., Duran E.A.
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Particle swarm optimization based Liu-type estimator
Communications in Statistics - Theory and Methods, 2016In this study, a new method for the estimation of the shrinkage and biasing parameters of Liu-type estimator is proposed. Because k is kept constant and d is optimized in Liu’s method, a (k, d) pair is not guaranteed to be the optimal point in terms of the mean square error of the parameters. The optimum (k, d) pair that minimizes the mean square error,
Inan, Deniz +4 more
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Influence Diagnostics in Modified Liu-type Estimator
Calcutta Statistical Association Bulletin, 2016In regression, it is of interest to detect anomalous observations that exert an unduly large influence on the least squares (LS) analysis. Frequently, the existence of influential data is complicated by the presence of collinearity (see, e.g., Walker and Birch [1] ).
Hadi Emami, Mostafa Emami
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Liu-type Estimator in the Bell Regression Model
2022This study proposes a new estimator used in the case of multicollinearity problems in the Bell regression model that is an alternative model for the Poissonregression model. The Bell regression model is used to solve the overdispersion problem. Generally, the maximum likelihood estimation (MLE) method is used toestimate the parameters of the Bell ...
IŞILAR, Melike, BULUT, Y. Murat
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Generalized Liu Type Estimators Under Zellner's Balanced Loss Function
Communications in Statistics - Theory and Methods, 2005ABSTRACT In regression analysis, ridge regression estimators and Liu type estimators are often used to overcome the problem of multicollinearity. These estimators have been evaluated using the risk under quadratic loss criterion, which places sole emphasis on estimators′ precision. The traditional mean square error (MSE) as the measure of efficiency of
Akdeniz F., Wan A.T.K., Akdeniz E.
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Robust Liu-type estimator for regression based on M-estimator
Communications in Statistics - Simulation and Computation, 2015ABSTRACTThe problem of multicollinearity and outliers in the dataset can strongly distort ordinary least-square estimates and lead to unreliable results. We propose a new Robust Liu-type M-estimator to cope with this combined problem of multicollinearity and outliers in the y-direction.
Ertaş H., Kaçıranlar S., Güler H.
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Using Liu-Type Estimator to Combat Collinearity
Communications in Statistics - Theory and Methods, 2003Linear regression model and least squares method are widely used in many fields of natural and social sciences. In the presence of collinearity, the least squares estimator is unstable and often gives misleading information. Ridge regression is the most common method to overcome this problem.
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