Results 231 to 240 of about 16,607 (260)

Modified almost unbiased Liu estimator in logistic regression

Communications in Statistics - Simulation and Computation, 2019
This paper focuses on introducing a new parameter estimator to the logistic regression model when the multicollinearity presents.
Nagarajah Varathan, Pushpakanthie Wijekoon   +1 more
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Logistic Liu Estimator under stochastic linear restrictions

Statistical Papers, 2016
The general logistic regression model is considered. The following stochastic prior information is given $h=H\beta+v$, $E(v)=0$. Here $h$ is a known vector, $H$ is a full rank known matrix, $\beta$ is a vector of regression coefficients to be estimated, and $v$ is a random vector of disturbances with known positive definite variance-covariance matrix ...
Nagarajah Varathan, Pushpakanthie Wijekoon   +1 more
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On the Stein-Type Liu Estimator and Positive-Rule Stein-Type Liu Estimator in Multiple Linear Regression Models

Communications in Statistics - Theory and Methods, 2012
In this article, the Stein-type Liu estimator and positive-rule Stein-type Liu estimator are constructed for the parameter vector in a multiple linear model under a multicollinearity situation when it is suspected that the regression coefficients may be restricted to a subspace.
Jianwen Xu, Hu Yang
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More on the restricted Liu estimator in the logistic regression model

Communications in Statistics - Simulation and Computation, 2015
ABSTRACTSiray et al. proposed a restricted Liu estimator to overcome multicollinearity in the logistic regression model. They also used a Monte Carlo simulation to study the properties of the restricted Liu estimator. However, they did not present the theoretical result about the mean squared error properties of the restricted estimator compared to MLE,
Jibo Wu, Yasin Asar
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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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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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A New Liu-Ratio Estimator For Linear Regression Models

Istanbul Journal of Mathematics
Summary: In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables. Although there are various methods for estimating parameters, the most popular is the Ordinary Least Squares (OLS) method.
Giresunlu, İ. M., Akay, K. U., Ertan, E.   +2 more
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Modified restricted Liu estimator in logistic regression model

Computational Statistics, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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Improved Liu-type estimator in partial linear model

International Journal of Computer Mathematics, 2015
In this article, a Liu-type estimation is proposed for the vector-parameter in a partial linear model. This new estimator can be regarded as generalization of the restricted least-squares estimator, the restricted ridge estimator and the restricted Liu estimator. We also obtain the asymptotic distributional bias and risk of these estimators and we also
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