Certain Nonparametric Classification Rules and Their Asymptotic Efficiencies.
Abstract
Two nonparametric classification rules for c-univariate populations are proposed, one in which the probability of correct classification is a specified number and the other in which one has to evaluate the probability of correct classification. In each case the classification is with respect to the Chernoff-Savage (1958) class of statistics, with possible specialization to populations having different location shifts and different changes of scale. An optimum property, namely, the consistency of the classification procedure is established for the second rule, when the distributions are either fixed or near in the Pitman sense and are tending to a common distribution at a specified rate. (Modified author abstract)
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 1974
- Accession Number
- AD0778026
Entities
People
- Abhinav Gupta
- Z. Govindarajulu
Organizations
- University of Kentucky