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Applications of response dimension reduction in large p-small n problems
- Title
- Applications of response dimension reduction in large p-small n problems
- Authors
- Kim; Minjee; Yoo; Jae Keun
- Ewha Authors
- 유재근
- SCOPUS Author ID
- 유재근
- Issue Date
- 2024
- Journal Title
- Communications for Statistical Applications and Methods
- ISSN
- 2287-7843
- Citation
- Communications for Statistical Applications and Methods vol. 31, no. 2, pp. 191 - 202
- Keywords
- high-dimensional data analysis; large p-small n data; model-based reduction; multivariate regression; response dimension reduction
- Publisher
- Korean Statistical Society
- Indexed
- SCOPUS; KCI
- Document Type
- Article
- Abstract
- 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. © 2024 The Korean Statistical Society, and Korean International Statistical Society. All Rights Reserved.
- DOI
- 10.29220/CSAM.2024.31.2.191
- Appears in Collections:
- 자연과학대학 > 통계학전공 > Journal papers
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