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Performance of likelihood-based estimation methods for multilevel binary regression models

机译:基于似然估计的多层二元回归模型的性能

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摘要

By means of a fractional factorial simulation experiment, we compare the performance of penalised quasi-likelihood (PQL), non-adaptive Gaussian quadrature and adaptive Gaussian quadrature in estimating parameters for multilevel logistic regression models. The comparison is done in terms of bias, mean-squared error (MSE), numerical convergence and computational efficiency. It turns out that in terms of MSE, standard versions of the quadrature methods perform relatively poorly in comparison with PQL.
机译:通过分数阶乘模拟实验,我们比较了在估计多级Logistic回归模型的参数时,惩罚拟似然(PQL),非自适应高斯正交和自适应高斯正交的性能。根据偏差,均方误差(MSE),数值收敛和计算效率进行比较。事实证明,就MSE而言,正交方法的标准版本与PQL相比性能相对较差。

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