Two stage multiple comparisons with the average for exponential location parameters under heteroscedasticity |
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Affiliation: | 1. TU Dortmund University, Vogelpothsweg 87, 44221, Dortmund, Germany;2. Maastricht University, Vijverdalseweg 1, 6226 NB, Maastricht, the Netherlands;3. UA Ruhr, Research Center Trustworthy Data Science and Security, 44227, Dortmund, Germany;1. Department of Mathematics, Indian Institute of Technology Kharagpur, WB 721 302, India;2. Applied Statistics Unit, Indian Statistical Institute Kolkata, WB 700 108, India;1. Department of Economics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan;2. Université de Sherbrooke, Département de mathématiques, Sherbrooke Qc, Canada, J1K 2R1;3. Rutgers University, Department of Statistics and Biostatistics, 501 Hill Center, Busch Campus, Piscataway, N.J., 08855, USA |
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Abstract: | In this article, two-stage procedures for multiple comparisons with the average for location parameters of two-parameter exponential distributions under heterocedasity including one- and two-sided confidence intervals are proposed. These intervals can be used to identify a subset which includes all no-worse-than-the-average treatments in an experimental design and to identify better-than-the-average, worse-than-the-average and not-much-different-from-the-average products in agriculture, stock market, medical research, and auto models. An upper limit of critical values are obtained using the recent techniques given in Lam (Proceedings of the Second International Advanced Seminar/Workshop on Inference Procedures Associated with Statistical Ranking and Selection, Sydney, Australia, August 1987; Comm. Statist. Simulation Comput. B17(3) (1988) 55). These approximate critical values are shown to have better results than the approximate critical values using the Bonferroni inequality developed in this paper. An example of comparing four drugs in the treatment of leukemia is given to demonstrate the proposed methodology. |
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