论文标题
vcbart：贝叶斯树的不同系数
VCBART: Bayesian trees for varying coefficients
论文作者
论文摘要
线性变化的系数模型提出了结果与协变量之间的线性关系，其中协变量效应被建模为附加效应修饰符的函数。尽管在统计和计量经济学中进行了悠久的研究和使用历史，但最先进的系数建模方法无法适应多变量效应修饰符，而无需施加限制性功能形式假设或涉及计算密集型高参数调谐。作为回应，我们引入了VCBART，该VCBART使用贝叶斯添加剂树在不同系数模型中灵活地估算了协变量。通过简单的默认设置，VCBART在协变量估计，不确定性量化和结果预测方面优于现有的变化系数方法。我们通过两个案例研究说明了VCBART的实用性：一个研究后期认知和社会经济地位衡量之间的关联在年龄和社会人口统计学方面有何不同，以及另一种估计城市犯罪的时间趋势在邻里水平上的变化。实施VCBART的R软件包可在https://github.com/skdeshpande91/vcbart上找到
The linear varying coefficient models posits a linear relationship between an outcome and covariates in which the covariate effects are modeled as functions of additional effect modifiers. Despite a long history of study and use in statistics and econometrics, state-of-the-art varying coefficient modeling methods cannot accommodate multivariate effect modifiers without imposing restrictive functional form assumptions or involving computationally intensive hyperparameter tuning. In response, we introduce VCBART, which flexibly estimates the covariate effect in a varying coefficient model using Bayesian Additive Regression Trees. With simple default settings, VCBART outperforms existing varying coefficient methods in terms of covariate effect estimation, uncertainty quantification, and outcome prediction. We illustrate the utility of VCBART with two case studies: one examining how the association between later-life cognition and measures of socioeconomic position vary with respect to age and socio-demographics and another estimating how temporal trends in urban crime vary at the neighborhood level. An R package implementing VCBART is available at https://github.com/skdeshpande91/VCBART
