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Science China Mathematics

, Volume 62, Issue 5, pp 979–998 | Cite as

A theoretic study of a distance-based regression model

  • Jialu Li
  • Wei Zhang
  • Sanguo Zhang
  • Qizhai LiEmail author
Articles

Abstract

The distance-based regression model has many applications in analysis of multivariate response regression in various fields, such as ecology, genomics, genetics, human microbiomics, and neuroimaging. It yields a pseudo F test statistic that assesses the relation between the distance (dissimilarity) of the subjects and the predictors of interest. Despite its popularity in recent decades, the statistical properties of the pseudo F test statistic have not been revealed to our knowledge. This study derives the asymptotic properties of the pseudo F test statistic using spectral decomposition under the matrix normal assumption, when the utilized dissimilarity measure is the Euclidean or Mahalanobis distance. The pseudo F test statistic with the Euclidean distance has the same distribution as the quotient of two Chi-squared-type mixtures. The denominator and numerator of the quotient are approximated using a random variable of the form \(\xi\chi_d^2+\eta\) and the approximate error bound is given. The pseudo F test statistic with the Mahalanobis distance follows an F distribution. In simulation studies, the approximated distribution well matched the “exact” distribution obtained by the permutation procedure. The obtained distribution was further validated on H1N1 influenza data, aging human brain data, and embryonic imprint data.

Keywords

distance-based regression Euclidean pseudo F test statistic Mahalanobis 

MSC(2010)

62H15 62J99 

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Notes

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 11722113). The authors thank the anonymous reviewers for their insightful comments, which improve the manuscript substantially.

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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mathematics and StatisticsBeijing Institute of TechnologyBeijingChina
  2. 2.Biostatistics and Bioinformatics BranchNational Institute of Child Health and Human DevelopmentBethesdaUSA
  3. 3.School of Mathematical SciencesUniversity of Chinese Academy of SciencesBeijingChina
  4. 4.Key Laboratory of Big Data Mining and Knowledge ManagementChinese Academy of SciencesBeijingChina
  5. 5.LSC, NCMIS, Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijingChina

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