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.
L. M. Penas and J. K. Patel, "Classifying K Normal Populations With Respect To A Control: Making At Most M(0 < M < K) Incorrect Decisions," Communications in Statistics - Theory and Methods, vol. 16, no. 8, pp. 2287 - 2311, Taylor and Francis Group; Taylor and Francis, Jan 1987.
The definitive version is available at https://doi.org/10.1080/03610928708829507
Mathematics and Statistics
Keywords and Phrases
classification; location parameter; ranking and selection; scale parameter; stochastically larger
International Standard Serial Number (ISSN)
Article - Journal
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01 Jan 1987