Communications for Statistical Applications and Methods

  • 제31권2호
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  • Pages.191-202
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  • 2024
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Applications of response dimension reduction in large p-small n problems

  • Minjee Kim (Department of Statistics, Ewha Womans University) ;
  • Jae Keun Yoo (Department of Statistics, Ewha Womans University)
  • 투고 : 2024.01.04
  • 심사 : 2024.02.05
  • 발행 : 2024.03.31
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초록

The goal of this paper is to show how multivariate regression analysis with high-dimensional responses is facilitated by the response dimension reduction. Multivariate regression, characterized by multi-dimensional response variables, is increasingly prevalent across diverse fields such as repeated measures, longitudinal studies, and functional data analysis. One of the key challenges in analyzing such data is managing the response dimensions, which can complicate the analysis due to an exponential increase in the number of parameters. Although response dimension reduction methods are developed, there is no practically useful illustration for various types of data such as so-called large p-small n data. This paper aims to fill this gap by showcasing how response dimension reduction can enhance the analysis of high-dimensional response data, thereby providing significant assistance to statistical practitioners and contributing to advancements in multiple scientific domains.

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과제정보

For Jae Keun Yoo and Minjee Kim, this work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Education (RS-2023-00240564 and RS-2023-00217022).

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