Rhizosphere Microbial Community Promotes Nitrogen Availability and Yield in Water-Saving and Drought-Resistance Rice

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.

Applications of response dimension reduction in large p-small n problems

/pdf/seedcca-an-integrated-r-package-for-canonical-correlation-3t9jegqrmz.pdf

SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares

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SEEDCCA: An Integrated R-Package for Canonical Correlation Analysis and Partial Least Squares