Comparison of hyperbolic and constant width simultaneous confidence bands in multiple linear regression under MVCS criterion

W. Liu, A. J. Hayter, W. W. Piegorsch, P. Ah-Kine

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

A simultaneous confidence band provides useful information on the plausible range of the unknown regression model, and different confidence bands can often be constructed for the same regression model. For a simple regression line, Liu and Hayter [W. Liu, A.J. Hayter, Minimum area confidence set optimality for confidence bands in simple linear regression, J. Amer. Statist. Assoc. 102 (477) (2007) pp. 181-190] proposed the use of the area of the confidence set corresponding to a confidence band as an optimality criterion in comparison of confidence bands; the smaller the area of the confidence set, the better the corresponding confidence band. This minimum area confidence set (MACS) criterion can be generalized to a minimum volume confidence set (MVCS) criterion in the study of confidence bands for a multiple linear regression model. In this paper hyperbolic and constant width confidence bands for a multiple linear regression model over a particular ellipsoidal region of the predictor variables are compared under the MVCS criterion. It is observed that whether one band is better than the other depends on the magnitude of one particular angle that determines the size of the predictor variable region. When the angle and hence the size of the predictor variable region is small, the constant width band is better than the hyperbolic band but only marginally. When the angle and hence the size of the predictor variable region is large the hyperbolic band can be substantially better than the constant width band. Crown

Original languageEnglish (US)
Pages (from-to)1432-1439
Number of pages8
JournalJournal of Multivariate Analysis
Volume100
Issue number7
DOIs
StatePublished - Aug 1 2009

Keywords

  • Confidence sets
  • Linear regression
  • Simultaneous confidence bands
  • Statistical inference

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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