Bootstrapping for multivariate linear regression models

Research output: Contribution to journalArticle

Abstract

The multivariate linear regression model is an important tool for investigating relationships between several response variables and several predictor variables. The primary interest is in inference about the unknown regression coefficient matrix. We propose multivariate bootstrap techniques as a means for making inferences about the unknown regression coefficient matrix. These bootstrapping techniques are extensions of those developed in Freedman (1981), which are only appropriate for univariate responses. Extensions to the multivariate linear regression model are made without proof. We formalize this extension and prove its validity. A real data example and two simulated data examples which offer some finite sample verification of our theoretical results are provided.

Original languageEnglish (US)
Pages (from-to)141-149
Number of pages9
JournalStatistics and Probability Letters
Volume134
DOIs
StatePublished - Mar 2018
Externally publishedYes

Keywords

  • Multivariate bootstrap
  • Multivariate linear regression model
  • Residual bootstrap

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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