On the stochastic restricted Liu-type maximum likelihood estimator in logistic regression

@article{Wu2017OnTS,
  title={On the stochastic restricted Liu-type maximum likelihood estimator in logistic regression},
  author={Jibo Wu and Yasin Asar},
  journal={arXiv: Methodology},
  year={2017}
}
In order to overcome multicollinearity, we propose a stochastic restricted Liu-type max- imum likelihood estimator by incorporating Liu-type maximum likelihood estimator (Inan and Erdo- gan, 2013) to the logistic regression model when the linear restrictions are stochastic. We also discuss the properties of the new estimator. Moreover, we give a method to choose the biasing parameter in the new estimator. Finally, a simulation study is given to show the performance of the new estimator. 

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TLDR
The restricted Liu estimator is compared with MLE, RMLE and Liu estimators in the mean squared error sense and a method to choose a biasing parameter is presented.
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In order to overcome the problem of multicollinearity in logistic regression, several researchers proposed alternative estimators when exact linear restrictions are available in addition to sample
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In the logistic regression, it is known that multicollinearity affects the variance of Maximum Likelihood Estimator (MLE). To overcome this issue, several researchers proposed alternative estimators
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The primary interest of this article is to introduce a Liu-type estimator that had a smaller total mean squared error (MSE) than the Schaefer's ridge estimator under certain conditions.
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New shrinkage parameters for the Liu-type estimators in the Liu (2003) in the logistic regression model defined by Huang (2012) are introduced in order to decrease the variance and overcome the problem of multicollinearity.
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In this article, we propose a restricted Liu regression estimator (RLRE) for estimating the parameter vector, β, in the presence of multicollinearity, when the dependent variable is binary and it is
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Schaefer et al. [15] proposed a ridge logistic estimator in logistic regres- sion model. In this paper a new estimator based on the ridge logistic estimator is introduced in logistic regression model
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This paper introduces a shrinkage estimator for the logit model which is a generalization of the estimator proposed by Liu (1993) for the linear regression. This new estimation method is suggested
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Abstract Linear 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
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