Results 201 to 210 of about 11,350 (238)

Liu-type estimator for the gamma regression model

Communications in Statistics - Simulation and Computation, 2018
In this paper, we propose a new biased estimator called Liu-type estimator in gamma regression models in the presence of collinearity.
Zakariya Yahya Algamal, Yasin Asar
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Evaluation of the predictive performance of the Liu type estimator

Communications in Statistics - Simulation and Computation, 2016
Multiple linear regression models are frequently used in predicting (forecasting) unknown values of the response variable y. In this case, a regression model ability to produce an adequate prediction equation is of prime importance. This paper discusses the predictive performance of the Liu estimator compared to ordinary least squares, as well as to ...
Dawoud I., Kaçiranlar S.
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Robust Liu-type estimator for regression based on M-estimator

Communications in Statistics - Simulation and Computation, 2015
ABSTRACTThe 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.
Hasan Ertas, Selahattin Kaçiranlar, Hüseyin Güler   +2 more
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Liu-type Estimator in the Bell Regression Model

2022
This 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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Developing a Liu‐type estimator in beta regression model

Concurrency and Computation: Practice and Experience, 2021
AbstractThe beta regression model is a commonly used when the response variable has the form of fractions or percentages. The maximum likelihood (ML) estimator is used to estimate the regression coefficients of this model. However, it is known that multicollinearity problem affects badly the variance of ML estimator.
Zakariya Yahya Algamal, Mohamed Reda Abonazel   +1 more
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On the Principal Component Liu-type Estimator in Linear Regression

Communications in Statistics - Simulation and Computation, 2014
In this article, we present a principal component Liu-type estimator (LTE) by combining the principal component regression (PCR) and LTE to deal with the multicollinearity problem. The superiority of the new estimator over the PCR estimator, the ordinary least squares estimator (OLSE) and the LTE are studied under the mean squared error matrix.
Jibo Wu, Hu Yang 0001
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New Shrinkage Parameters for the Liu-type Logistic Estimators

Communications in Statistics - Simulation and Computation, 2015
The binary logistic regression is a widely used statistical method when the dependent variable has two categories. In most of the situations of logistic regression, independent variables are collinear which is called the multicollinearity problem. It is known that multicollinearity affects the variance of maximum likelihood estimator (MLE) negatively ...
Yasin Asar, Asir Genç
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Almost unbiased Liu-type estimators in gamma regression model

Journal of Computational and Applied Mathematics, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yasin Asar, Merve Korkmaz
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Influence Diagnostics in Modified Liu-type Estimator

Calcutta Statistical Association Bulletin, 2016
In 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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Adjustive Liu-Type Estimators in Linear Regression Models

Communications in Statistics - Simulation and Computation, 2010
In this article, we aim to put forward the notion of adjustive Liu-type estimator (ALTE) in the linear regression model. First, the explicit expression of the optimal selection of the adjustive factors is derived under the PRESS criterion through matrix techniques. Then, the results are applied to the dataset on Portland cement.
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