统计与决策2025，Vol.41Issue(5)：37-42,6.DOI:10.13546/j.cnki.tjyjc.2025.05.006
基于横截面和纵向信息的函数型多重插补方法
Functional Multiple Imputation Method Based on Cross-sectional and Longitudinal Information
摘要
Abstract
Functional data is a type of complex nonlinear structured data,which is often presented and stored in the form of functions(curves).However,in the process of data collection,missing data is inevitable.This paper proposes a Missforest Combin-ing Gaussian Processes(MFGP)method based on cross-sectional and longitudinal information.Inspired by the ensemble models,the method integrates imputation based on Missforest model(MF)with prediction based on Gaussian processes(GP),effectively in-tegrates cross-sectional and longitudinal information of functional data to enhance imputation accuracy.Meanwhile,the results of simulation data interpolation experiment and stock data example analysis show that under the missing ratio of 5%to 55%,MFGP has a significant imputation advantage over seven other imputation methods,namely mean imputation,Hot.deck,SFI,HFI,MICE,MF and GP,and the obtained data is more consistent with the original data.关键词
机器学习/缺失数据/多重插补/集成模型Key words
machine learning/missing data/multiple imputation/ensemble model分类
数理科学引用本文复制引用
高海燕,李唯欣..基于横截面和纵向信息的函数型多重插补方法[J].统计与决策,2025,41(5):37-42,6.基金项目
国家社会科学基金资助项目(19XTJ002) （19XTJ002）
甘肃省自然科学基金资助项目(23JRRA1186) （23JRRA1186）
甘肃省高校青年博士支持项目(2025QB-058) （2025QB-058）
