
Accession Number : AD0778026
Title : Certain Nonparametric Classification Rules and Their Asymptotic Efficiencies.
Descriptive Note : Technical rept.,
Corporate Author : KENTUCKY UNIV LEXINGTON DEPT OF STATISTICS
Personal Author(s) : Govindarajulu,Z. ; Gupta,A. K.
Report Date : APR 1974
Pagination or Media Count : 20
Abstract : Two nonparametric classification rules for cunivariate 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 ChernoffSavage (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)
Descriptors : *Statistical analysis, *Distribution functions, Estimates, Random variables, Theorems
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE