Classifying K Normal Populations With Respect To A Control: Making At Most M(0 < M < K) Incorrect Decisions


This paper is concerned with classifying k normal populations as better or worse than a control with a goal of making at most m(0 < m < k) incorrect decisions. These populations are first compared by their means assuming their variances to be equal and known. Next, the comparisons are based on their variances assuming their means to be known or unknown. Rules are proposed when the control is known or unknown and exact solutions are tabulated. A modified goal of making at most m1 incorrect decisions in classifying better populations, and making at most m2 (0 < m«j + m 2 < k) incorrect decisions in classifying _ worse populations is also investigated. Generalizations to location and scale parameter families of distributions are discussed. © 1987, Taylor & Francis Group, LLC. All rights reserved.


Mathematics and Statistics

Keywords and Phrases

classification; location parameter; ranking and selection; scale parameter; stochastically larger

International Standard Serial Number (ISSN)

1532-415X; 0361-0926

Document Type

Article - Journal

Document Version


File Type





© 2023 Taylor and Francis Group; Taylor and Francis, All rights reserved.

Publication Date

01 Jan 1987