Two-stage multiple comparisons with the average for normal distributions under heteroscedasticity

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Title:	Two-stage multiple comparisons with the average for normal distributions under heteroscedasticity
Other Titles:	雙階段的常態分配與平均的多重比較程序當變異數不等時
Authors:	吳淑妃;Wu, Shu-fei;Chen, Hubert J.
Contributors:	淡江大學統計學系
Keywords:	Two-stage procedure;Monte-Carlo techniques
Date:	2000-04-28
Issue Date:	2009-11-30 12:54:56 (UTC+8)
Publisher:	Elsevier
Abstract:	In this article we propose a two-stage procedure for multiple comparisons with the average for normal distributions under heteroscedasity. One-sided 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 obatined using Bonferroni inequality. But the approximate values are shown to be too conservative compared with the simulation critical values in Table 7. Therefore, simulation critical values should be used in our multiple comparison procedures. Statistical tables and software programs are provided for use in practice.
Relation:	Computational Statistics and Data Analysis 33(2), pp.201-213
DOI:	10.1016/S0167-9473(99)00052-3
Appears in Collections:	[Graduate Institute & Department of Statistics] Journal Article

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