Selection of Largest Multiple Correlation Coefficients: Asymptotic Case.
Abstract
The paper considers the problem of selection of t largest from among k multiple correlation coefficients, each arising from one of k independent p-variate normal populations with unknown mean vectors and unknown covariance matrices. A selection procedure based on a natural ordering of the squared sample multiple correlation coefficients is proposed and the infimum of the probability of a correct selection over a specified preference zone in the parameter space is evaluated for large values of the common sample size n. A table giving values of n for preassigned minimal probability of a correct selection is appended. Certain decision-theoretic properties of the proposed procedure and some applications, especially the case of the simple correlation coefficients arising in bivariate normal populations, are discussed. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 01, 1972
- Accession Number
- AD0737296
Entities
People
- Herbert Solomon
- M. Haseeb Rizvi
Organizations
- Stanford University