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首页> 外文期刊>Communications in Mathematical Biology and Neuroscience >Firth bias correction for estimating variance components of logistics linear mixed model using penalized quasi likelihood method
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Firth bias correction for estimating variance components of logistics linear mixed model using penalized quasi likelihood method

机译:圆偏压校正,用于使用惩罚的准可能性方法估算物流线性混合模型的差异分量

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Firth bias correction originally was applied to correct bias of the variance components estimator that obtained by the maximum likelihood method. Extensive research has shown that Firth bias correction is powerful to reduce bias for normal distributed response model. Questions have been raised about the use of Firth bias correction in binomial distributed response model which has under dispersion problem. The motivation of this study is giving contribution to exploring the Firth bias correction for binomial distributed response model. The binomial distributed response model which is estimated by the maximum likelihood method obtain an under-dispersion estimator. Therefore, the Penalized Quasi-Likelihood (PQL) is used as alternative numerical method to estimate the model. This paper aims to investigate whether the Firth method can reduce bias of the variance components using the PQL technique in longitudinal data.
机译:最初的围绕偏置校正被应用于通过最大似然方法获得的差异分量估计器的偏置。广泛的研究表明,Firth偏压校正功能强大以减少正常分布式响应模型的偏置。关于在分散问题下的二项式分布式响应模型中使用Firth偏置校正的问题已经提出了问题。该研究的动机是为探索二项式分布式响应模型的围绕偏压校正提供贡献。由最大似然法估计的二项分分布式响应模型获得了欠散估计器。因此,惩罚的准可能性(PQL)用作估计模型的替代数值方法。本文旨在研究FiRTH方法是否可以使用PQL技术在纵向数据中减少方差分量的偏差。

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