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

Hsu, Lifang; Panchapakesan, S.

doi:10.1007/0-8176-4422-9_7

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…For this case of 1 ≤ m ≤ k − t, it can be seen from Santner (1976) and Hsu and Panchapakesan (2005a) that, with * = 0 (i.e., no preference-zone), we can have the minimum guaranteed PCS equal to P * only for For this case of 1 ≤ m ≤ k − t, it can be seen from Santner (1976) and Hsu and Panchapakesan (2005a) that, with * = 0 (i.e., no preference-zone), we can have the minimum guaranteed PCS equal to P * only for…”

Section: Downloaded By [Erciyes University] At 14:39 22 December 2014mentioning

confidence: 90%

“…Hsu and Panchapakesan (2005b) have also proposed the same rule they proposed in Case 1, namely, R t HP . An unattractive feature of the rule R t GS in Case 2 is that the constant d does not depend on bound m for the subset size.…”

Section: Panchapakesanmentioning

confidence: 97%

“…Hsu and Panchapakesan (2005b) have introduced an additional condition to be satisfied by R t HP in order to make c depend upon n. It can be easily seen, from the form of the rule R t HP , that the constant c does not depend upon the sample size n. This is an unattractive feature of R t HP .…”

Section: Panchapakesanmentioning

confidence: 99%

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Restricted Subset Selection Procedures for Normal Means: A Brief Review with a Fresh Look at the Classical Formulations of Bechhofer and Gupta

Panchapakesan

2005

Communications in Statistics - Theory and Methods

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Consider k normal populations with unknown means and a common known variance.The populations associated with the t largest means are called the t best populations. We consider the problem of selecting a subset of the k populations whose size does not exceed m 1 ≤ m ≤ k so that the selected subset includes at least one of the t 1 ≤ t ≤ k − 1 best with a guaranteed probability. We provide a brief review of the literature on this restricted subset selection problem and, in doing so, provide a fresh look at the salient features of the two classical formulations, namely, the indifference-zone and subset selection approaches.

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Section: Downloaded By [Erciyes University] At 14:39 22 December 2014mentioning

confidence: 90%

Section: Panchapakesanmentioning

confidence: 97%

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Restricted Subset Selection Procedures for Normal Means: A Brief Review with a Fresh Look at the Classical Formulations of Bechhofer and Gupta

Panchapakesan

2005

Communications in Statistics - Theory and Methods

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“…The Gupta-Santner restricted subset selection rule was extended to unknown variances by Sullivan and Wilson (1989). We refer the reader to Hsu and Panchapakesan (2005) and Chen (2008Chen ( , 2010 for more recent studies of restricted subset selection procedures in simulation.…”

Section: Introductionmentioning

confidence: 99%

A subset selection procedure under input parameter uncertainty

Corlu

Biller

2013

2013 Winter Simulations Conference (WSC)

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This paper considers a stochastic system simulation with unknown input distribution parameters and assumes the availability of a limited amount of historical data for parameter estimation. We investigate how to account for parameter uncertainty -the uncertainty that is due to the estimation of the input distribution parameters from historical data of finite length -in a subset selection procedure that identifies the stochastic system designs whose sample means are within a user-specified distance of the best mean performance measure. We show that even when the number of simulation replications is large enough for the stochastic uncertainty to be negligible, the amount of parameter uncertainty in output data imposes a threshold on the user-specified distance for an effective use of the subset selection procedure for simulation. We demonstrate the significance of this effect of parameter uncertainty for a multi-item inventory system simulation in the presence of short demand histories.

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Closed testing procedure for comparing sizes of normal means based on ordered statistics

Imada

2024

Communications in Statistics - Theory and Methods

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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

Cited by 3 publications

References 5 publications

Restricted Subset Selection Procedures for Normal Means: A Brief Review with a Fresh Look at the Classical Formulations of Bechhofer and Gupta

Restricted Subset Selection Procedures for Normal Means: A Brief Review with a Fresh Look at the Classical Formulations of Bechhofer and Gupta

A subset selection procedure under input parameter uncertainty

Closed testing procedure for comparing sizes of normal means based on ordered statistics

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