论文标题
SPQR：半参数密度和分位数回归的R包装
SPQR: An R Package for Semi-Parametric Density and Quantile Regression
论文作者
论文摘要
我们开发了一个R软件包SPQR，该S SPQR在Xu and Reich（2021）中实现了半参数分位数回归（SPQR）方法。该方法首先使用单调的花纹拟合柔性密度回归模型，其权重被建模为使用人工神经网络的数据依赖性功能。随后，可以获得条件密度和分位过程的估计。与许多假设线性模型的分位数回归方法不同，SPQR几乎允许协变量与响应分布之间的任何关系，包括非线性效应以及对不同分数水平的不同影响。为了提高SPQR的可解释性和透明度，使用Apley和Zhu（2020）开发的模型无关统计量来估计和可视化协变量效应及其对分数功能的相对重要性。在本文中，我们详细介绍了如何在SPQR中实现此框架，并说明了如何通过模拟和真实的数据示例在实践中使用该软件包。
We develop an R package SPQR that implements the semi-parametric quantile regression (SPQR) method in Xu and Reich (2021). The method begins by fitting a flexible density regression model using monotonic splines whose weights are modeled as data-dependent functions using artificial neural networks. Subsequently, estimates of conditional density and quantile process can all be obtained. Unlike many approaches to quantile regression that assume a linear model, SPQR allows for virtually any relationship between the covariates and the response distribution including non-linear effects and different effects on different quantile levels. To increase the interpretability and transparency of SPQR, model-agnostic statistics developed by Apley and Zhu (2020) are used to estimate and visualize the covariate effects and their relative importance on the quantile function. In this article, we detail how this framework is implemented in SPQR and illustrate how this package should be used in practice through simulated and real data examples.
