Selecting the Best Unknown Mean from Normal Populations Having a Common Unknown Coefficient of Variation

Shanti S. Gupta and TaChen Liang
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Shanti S. Gupta: Purdue University, Department of Statistics
TaChen Liang: Purdue University, Department of Statistics

A chapter in Mathematical Statistics and Probability Theory, 1987, pp 97-112 from Springer

Abstract: Abstract This paper deals with the problem of selecting the population associated with the largest unknown mean from several normal populations having a common unknown coefficient of variation. Both subset selection and indifference zone approaches are studied. Based on the observed sample means and sample standard deviations, a subset selection rule is proposed. Some properties related to this selection rule are discussed. For the indifference zone approach, a two-stage elimination type selection rule is considered. If the experimenter has some prior knowledge about an upper bound on the unknown means, a modification is introduced to reduce the size of the selected subset at the first stage and also to reduce the sample size at the second stage. An example is provided which indicates that the saving in the total sample size is quite significant if this prior knowledge is taken into consideration in designing the selection rule. It is shown how to implement the above selection rules by using several existing tables.

Keywords: Selection Rule; Subset Selection; Sample Standard Deviation; Correct Selection; Good Population (search for similar items in EconPapers)
Date: 1987
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-009-3965-3_10

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DOI: 10.1007/978-94-009-3965-3_10

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