### Abstract

Original language | English |
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Place of Publication | Eindhoven |

Publisher | Technische Universiteit Eindhoven |

Number of pages | 12 |

Publication status | Published - 1991 |

### Publication series

Name | Memorandum COSOR |
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Volume | 9123 |

ISSN (Print) | 0926-4493 |

### Fingerprint

### Cite this

*Distribution theory for selection from logistic populations*. (Memorandum COSOR; Vol. 9123). Eindhoven: Technische Universiteit Eindhoven.

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*Distribution theory for selection from logistic populations*. Memorandum COSOR, vol. 9123, Technische Universiteit Eindhoven, Eindhoven.

**Distribution theory for selection from logistic populations.** / Laan, van der, P.

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 -