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Parametric modeling of quantile regression coefficient functions with count data

机译：数值回归系数函数与计数数据的参数建模

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摘要

Applying quantile regression to count data presents logical and practical complications which are usually solved by artificially smoothing the discrete response variable through jittering. In this paper, we present an alternative approach in which the quantile regression coefficients are modeled by means of (flexible) parametric functions. The proposed method avoids jittering and presents numerous advantages over standard quantile regression in terms of computation, smoothness, efficiency, and ease of interpretation. Estimation is carried out by minimizing a "simultaneous" version of the loss function of ordinary quantile regression. Simulation results show that the described estimators are similar to those obtained with jittering, but are often preferable in terms of bias and efficiency. To exemplify our approach and provide guidelines for model building, we analyze data from the US National Medical Expenditure Survey. All the necessary software is implemented in the existing R package qrcm.
机译：将量数回归应用于计数数据显示逻辑和实际并发症，这些并发症通常通过抖动通过抖动人为平滑离散响应变量来解决。在本文中，我们提出了一种替代方法，其中定量回归系数通过（柔性）参数函数来建模。该方法避免了抖动，在计算，平滑度，效率和易于解释方面，在标准数量回归中呈现众多优点。通过最小化普通分量回归的“同时”版本的“同时”版本来执行估计。仿真结果表明，所描述的估计器类似于用抖动获得的估计器，但在偏差和效率方面通常是优选的。为了举例说明我们的方法并为模型建设提供指导方针，我们分析来自美国国家医疗支出调查的数据。所有必要的软件都在现有的R包QRCM中实现。

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