Abstract
| Original language | English |
|---|---|
| Place of Publication | Eindhoven |
| Publisher | Technische Universiteit Eindhoven |
| Number of pages | 12 |
| Publication status | Published - 1991 |
Publication series
| Name | Memorandum COSOR |
|---|---|
| Volume | 9123 |
| ISSN (Print) | 0926-4493 |
Fingerprint
Cite this
}
Distribution theory for selection from logistic populations. / Laan, van der, P.
Eindhoven : Technische Universiteit Eindhoven, 1991. 12 p. (Memorandum COSOR; Vol. 9123).Research output: Book/Report › Report › Academic
TY - BOOK
T1 - Distribution theory for selection from logistic populations
AU - Laan, van der, P.
PY - 1991
Y1 - 1991
N2 - Assume k (integer k \geq 2) independent populations \pi_1, \pi_2, ..., \pi_k are given. The associated independent random variables X_1, X_2, ..., X_k are Logistically distributed with unknown means \mu_1, \mu_2, ..., \mu_k, respectively, and common known variance. The goal is to select the best population, this is the population with the largest mean. Some distributional results are derived for subset selection as well as for the indifference zone approach. The probability of correct selection is determined. Exact and numerical results concerning the expected subset size are presented for the subset selection approach. Finally, some remarks are made for a generalized selection goal using subset selection. This goal is to select a non-empty subset of populations that contains at least one \epsilon-best (almost best) treatment with confidence level P*. For a set of populations an \epsilon-best reatment is defined as a treatment with location parameter on a distance less than or equal to \epsilon (\epsilon \geq 0) from the best population.
AB - Assume k (integer k \geq 2) independent populations \pi_1, \pi_2, ..., \pi_k are given. The associated independent random variables X_1, X_2, ..., X_k are Logistically distributed with unknown means \mu_1, \mu_2, ..., \mu_k, respectively, and common known variance. The goal is to select the best population, this is the population with the largest mean. Some distributional results are derived for subset selection as well as for the indifference zone approach. The probability of correct selection is determined. Exact and numerical results concerning the expected subset size are presented for the subset selection approach. Finally, some remarks are made for a generalized selection goal using subset selection. This goal is to select a non-empty subset of populations that contains at least one \epsilon-best (almost best) treatment with confidence level P*. For a set of populations an \epsilon-best reatment is defined as a treatment with location parameter on a distance less than or equal to \epsilon (\epsilon \geq 0) from the best population.
M3 - Report
T3 - Memorandum COSOR
BT - Distribution theory for selection from logistic populations
PB - Technische Universiteit Eindhoven
CY - Eindhoven
ER -