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On the restricted almost unbiased Liu estimator in the logistic regression model

机译:Logistic回归模型中的受限几乎无偏Liu估计

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摘要

It is known that when the multicollinearity exists in the logistic regression model, variance of maximum likelihood estimator is unstable. As a remedy, in the context of biased shrinkage Liu estimation, Chang introduced an almost unbiased Liu estimator in the logistic regression model. Making use of his approach, when some prior knowledge in the form of linear restrictions are also available, we introduce a restricted almost unbiased Liu estimator in the logistic regression model. Statistical properties of this newly defined estimator are derived and some comparison results are also provided in the form of theorems. A Monte Carlo simulation study along with a real data example are given to investigate the performance of this estimator.
机译:已知当逻辑回归模型中存在多重共线性时,最大似然估计器的方差不稳定。作为一种补救措施,在有偏差的Liu估计的背景下,Chang在logistic回归模型中引入了几乎无偏的Liu估计。利用他的方法,当还可以获得线性限制形式的一些先验知识时,我们在逻辑回归模型中引入了一个受限的,几乎无偏的Liu估计量。推导了这个新定义的估计量的统计性质,并以定理的形式提供了一些比较结果。给出了蒙特卡洛模拟研究以及实际数据示例,以研究该估计器的性能。

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