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On the performance of L_2E estimation in modelling heterogeneous count responses with extreme values

机译:L_2E估计在建模具有极值的异类计数响应中的性能

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In healthcare studies, count data sets measured with covariates often exhibit heterogeneity and contain extreme values. To analyse such count data sets, we use a finite mixture of regression model framework and investigate a robust estimation approach, called the L_2E [D.W. Scott, On fitting and adapting of density estimates, Comput. Sci. Stat. 30 (1998), pp. 124-133], to estimate the parameters. The L_2E is based on an integrated L_2 distance between parametric conditional and true conditional mass functions. In addition to studying the theoretical properties of the L_2E estimator, we compare the performance of L_2E with the maximum likelihood (ML) estimator and a minimum Hellinger distance (MHD) estimator via Monte Carlo simulations for correctly specified and gross-error contaminated mixture of Poisson regression models. These show that the L_2E is a viable robust alternative to the ML and MHD estimators. More importantly, we use the L_2E to perform a comprehensive analysis of a Western Australia hospital inpatient obstetrical length of stay (LOS) (in days) data that contains extreme values. It is shown that the L_2E provides a two-component Poisson mixture regression fit to the LOS data which is better than those based on the ML and MHD estimators. The L_2E fit identifies admission type as a significant covariate that profiles the predominant subpopulation of normal-stayers as planned patients and the small subpopulation of long-stayers as emergency patients.
机译:在医疗保健研究中,用协变量测量的计数数据集通常表现出异质性并包含极值。为了分析这种计数数据集,我们使用回归模型框架的有限混合并研究一种称为L_2E [D.W.斯科特,关于密度估计的拟合和调整,计算机。科学统计30(1998),第124-133页]估计参数。 L_2E基于参数条件质量函数和真实条件质量函数之间的积分L_2距离。除了研究L_2E估计量的理论性质外,我们还通过蒙特卡罗模拟对正确确定且受严重误差污染的Poisson混合物,将L_2E的性能与最大似然(ML)估计量和最小Hellinger距离(MHD)估计量进行比较回归模型。这些表明,L_2E是ML和MHD估计器的可行稳健替代方案。更重要的是,我们使用L_2E对包含极端值的西澳大利亚州医院住院产科住院天数(LOS)(以天为单位)进行全面分析。结果表明,L_2E对LOS数据提供了两成分的Poisson混合回归拟合,优于基于ML和MHD估计量的回归数据。 L_2E拟合将入院类型识别为重要的协变量,该变量描述了计划住院患者中正常住院者的主要亚人群和急诊患者中长期住院者的小亚人群。

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