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Quantile regression for overdispersed count data: a hierarchical method
Journal of Statistical Distributions and Applications Pub Date : 2017-11-01 , DOI: 10.1186/s40488-017-0073-4
Peter Congdon

Generalized Poisson regression is commonly applied to overdispersed count data, and focused on modelling the conditional mean of the response. However, conditional mean regression models may be sensitive to response outliers and provide no information on other conditional distribution features of the response. We consider instead a hierarchical approach to quantile regression of overdispersed count data. This approach has the benefits of effective outlier detection and robust estimation in the presence of outliers, and in health applications, that quantile estimates can reflect risk factors. The technique is first illustrated with simulated overdispersed counts subject to contamination, such that estimates from conditional mean regression are adversely affected. A real application involves ambulatory care sensitive emergency admissions across 7518 English patient general practitioner (GP) practices. Predictors are GP practice deprivation, patient satisfaction with care and opening hours, and region. Impacts of deprivation are particularly important in policy terms as indicating effectiveness of efforts to reduce inequalities in care sensitive admissions. Hierarchical quantile count regression is used to develop profiles of central and extreme quantiles according to specified predictor combinations.

中文翻译:

超分散计数数据的分位数回归:一种分层方法

广义泊松回归通常用于过度分散的计数数据,并且侧重于对响应的条件均值建模。但是,条件均值回归模型可能对响应离群值敏感,并且不提供有关响应的其他条件分布特征的信息。相反,我们考虑使用分层方法对过度分散的计数数据进行分位数回归。在存在异常值的情况下,这种方法具有有效的异常值检测和可靠估计的优势,并且在健康应用中,分位数估计值可以反映风险因素。首先用模拟的过度分散计数(受污染影响)来说明该技术,从而不利地影响了基于条件均值回归的估计。真正的应用涉及对7518名英国患者全科医生(GP)的门诊护理敏感的紧急入院。预测因素包括全科医生执业剥夺,患者对护理和开放时间的满意程度以及地区。就政策而言,剥夺的影响尤为重要,因为它表明了为减少护理敏感性住院病人中的不平等现象而进行的努力的有效性。分层分位数计数回归用于根据指定的预测变量组合来开发中心分位数和极限分位数。
更新日期:2017-11-01
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