Robustness of the multiple correlation coefficient when sampling from a mixture of two multivariate normal populations

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Abstract

The density of the multiple correlation coefficient is derived by direct integration when the sample covariance matrix has a linear non-central distribution. Using the density, we deduce the null and non-null distribution of the multiple correlation coefficient when sampling from a mixture of two multivariate normal populations with the same covariance matrix. We also compute actual significance levels of the test of the hypothesis Ho: P1.2.p-0 versus Ha:P1.2…p> 0, given the mixture model.

Original languageEnglish
Pages (from-to)1443-1457
Number of pages15
JournalCommunications in Statistics - Simulation and Computation
Volume19
Issue number4
DOIs
Publication statusPublished - Jan 1 1990

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Multiple Correlation Coefficient
Normal Population
Multivariate Normal
Covariance matrix
Sampling
Robustness
Sample Covariance Matrix
Significance level
Mixture Model
Null
Deduce

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation

Cite this

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