Scholarlink

<p>In practice, distributed data are often collected from different locations/times/populations /environments with non-negligible heterogeneity. In this paper, we consider inherently distributed data follow heterogeneous partially linear models with high-dimensional covariates, where each site involves a common parameter vector and site-specific nuisance functions. Three distributed estimators based on debiased machine learning are proposed to account for heterogeneity. The closed forms and asymptotic normal distributions of the proposed estimators have been explored and compared. Moreover, these estimators can be computed easily by transmitting some statistics from the local sites to the central site. The finite-sample performance is demonstrated through simulation studies and an application to Beijing multi-site air quality dataset is also provided.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Heterogeneity-aware debiased machine learning for high-dimensional partially linear models

  • 期刊论文

  • 发表日期：

    2025年9月24日

查看全文
  • Yining Wu,
  • Lei Wang,
  • Heng Lian

摘要

In practice, distributed data are often collected from different locations/times/populations /environments with non-negligible heterogeneity. In this paper, we consider inherently distributed data follow heterogeneous partially linear models with high-dimensional covariates, where each site involves a common parameter vector and site-specific nuisance functions. Three distributed estimators based on debiased machine learning are proposed to account for heterogeneity. The closed forms and asymptotic normal distributions of the proposed estimators have been explored and compared. Moreover, these estimators can be computed easily by transmitting some statistics from the local sites to the central site. The finite-sample performance is demonstrated through simulation studies and an application to Beijing multi-site air quality dataset is also provided.