A Restricted Subset Selection Rule for Selecting At Least One of the t Best Normal Populations in Terms of Their Means: Common Known Variance Case

2007 ◽  
pp. 117-131 ◽  
Author(s):  
Lifang Hsu ◽  
S. Panchapakesan
Keyword(s):  
Selection Rule ◽  
Subset Selection ◽  
Normal Populations

Some Optimal Selection Procedures for Dependent Normal Populations

1992 ◽  
Vol 42 (1-2) ◽  
pp. 19-28
Author(s):  
Tong—An Hsu
Keyword(s):  
Correlation Matrix ◽  
Normal Population ◽  
Subset Selection ◽  
Selection Procedure ◽  
Random Variable ◽  
Optimal Selection ◽  
Selection Procedures ◽  
Normal Populations ◽  
Optimal Subset ◽  
Equal Variance

We consider k dependent normal populations having known equal variance. The problem is to select the population associated with the largest mean. Let π1, ..., πk be k populations. We assume that the random variable Xi associated with πi ( i = 1, ..., k). Let X1, ..., Xk be k—variate normal distribution with unknown mean μ1, ... , μk and with equal variance σ2 and with correlation matrix { ρi j} For two case : (a) ρi j = aiaj for i ≠ j and (b) ρi j = ρ, for i ≠ j, we construct some optimal subset selection procedure to select a subset of the k dependent normal population containing the largest population mean. AMS ( 1980) subject classification : Primary 62F07 ; Secondary 62030

On the performance of Gupta's subset selection rule

2006 ◽  
Vol 136 (7) ◽  
pp. 2004-2019
Author(s):  
Klaus J. Miescke ◽  
Kenneth J. Ryan
Keyword(s):  
Selection Rule ◽  
Subset Selection
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