基于复合分位数回归的变系数模型平均
Average of Variable Coefficient Models Based on Compound Quantile Regression
谭蓉1
作者信息
- 1. 重庆师范大学数学科学学院,重庆 401331
- 折叠
摘要
针对变系数模型的平均问题,采用一种新的半参数建模策略,即通过轮换连续变量作为指标变量的方式构建一系列非嵌套的候选模型.基于复合分位数回归估计候选模型,运用弃一交叉验证准则选择权重进行模型平均预测.模拟研究表明该方法具有更好的有限样本性质,最后将该方法应用到Boston住房数据,进一步说明其准确性和有效性.
Abstract
To address the problem of averaging variable coefficient models,a new semi-para-metric modeling strategy is used,i.e.,a series of non-nested candidate models are construc-ted by rotating continuous variables as indicator variables.The candidate models are esti-mated based on composite quantile regression,and the discard-one cross-validation criterion is applied to select weights for model averaging prediction.A simulation study demonstrates the better finite-sample nature of the method,and finally the method is applied to Boston housing data to further illustrate its accuracy and validity.
关键词
复合分位数回归/模型平均/变系数模型Key words
composite quantile regression/model average/varying coefficient model引用本文复制引用
出版年
2025
NETL
NSTL
万方数据