Scholarlink

Functional data often present missing values. These pose challenges when performing functional principal components analysis, since it is not possible to compute the scores from the data. The existing method by Kraus (2015) allows to perform functional data completion in a univariate setting. In this paper, we propose an extension to the multivariate setting and show how statistical dependences between the functional curves aid in the imputation of missing data.

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

Completion of Partially Observed Multivariate Functional Data

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Marco Borriero,
  • Luigi Augugliaro,
  • Salvatore Latora,
  • Veronica Vinciotti

摘要

Functional data often present missing values. These pose challenges when performing functional principal components analysis, since it is not possible to compute the scores from the data. The existing method by Kraus (2015) allows to perform functional data completion in a univariate setting. In this paper, we propose an extension to the multivariate setting and show how statistical dependences between the functional curves aid in the imputation of missing data.