On selecting the best of two normal populations using a loss function

P. Laan, van der, C. Eeden, van

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The problem of selecting the best of two normal populations is considered. In selection problems the usual loss function is the 0-1 one, i.e. the selection goal is to bound, from below, probabilities of making «correct» selections. In the present paper a selection goal based on a general loss function is presented. The two populations have unknown location parameters and «good» populations are the ones with large values of this parameter. The selection rule is given and its performance is investigated. An application is presented. Similar results for the scale parameters of two gamma populations can be found in van der Laan and van Eeden (1996).
Original languageEnglish
Pages (from-to)159-170
JournalJournal of the Italian Statistical Society
Volume7
Issue number2
DOIs
Publication statusPublished - 1998

Fingerprint

Normal Population
Loss Function
Selection Rules
Location Parameter
Scale Parameter
Unknown Parameters
Loss function

Cite this

Laan, van der, P. ; Eeden, van, C. / On selecting the best of two normal populations using a loss function. In: Journal of the Italian Statistical Society. 1998 ; Vol. 7, No. 2. pp. 159-170.
@article{56382635cc024173909d7f97c7b055b0,
title = "On selecting the best of two normal populations using a loss function",
abstract = "The problem of selecting the best of two normal populations is considered. In selection problems the usual loss function is the 0-1 one, i.e. the selection goal is to bound, from below, probabilities of making «correct» selections. In the present paper a selection goal based on a general loss function is presented. The two populations have unknown location parameters and «good» populations are the ones with large values of this parameter. The selection rule is given and its performance is investigated. An application is presented. Similar results for the scale parameters of two gamma populations can be found in van der Laan and van Eeden (1996).",
author = "{Laan, van der}, P. and {Eeden, van}, C.",
year = "1998",
doi = "10.1007/BF03178926",
language = "English",
volume = "7",
pages = "159--170",
journal = "Journal of the Italian Statistical Society",
issn = "1121-9130",
publisher = "Physica-Verlag",
number = "2",

}

On selecting the best of two normal populations using a loss function. / Laan, van der, P.; Eeden, van, C.

In: Journal of the Italian Statistical Society, Vol. 7, No. 2, 1998, p. 159-170.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - On selecting the best of two normal populations using a loss function

AU - Laan, van der, P.

AU - Eeden, van, C.

PY - 1998

Y1 - 1998

N2 - The problem of selecting the best of two normal populations is considered. In selection problems the usual loss function is the 0-1 one, i.e. the selection goal is to bound, from below, probabilities of making «correct» selections. In the present paper a selection goal based on a general loss function is presented. The two populations have unknown location parameters and «good» populations are the ones with large values of this parameter. The selection rule is given and its performance is investigated. An application is presented. Similar results for the scale parameters of two gamma populations can be found in van der Laan and van Eeden (1996).

AB - The problem of selecting the best of two normal populations is considered. In selection problems the usual loss function is the 0-1 one, i.e. the selection goal is to bound, from below, probabilities of making «correct» selections. In the present paper a selection goal based on a general loss function is presented. The two populations have unknown location parameters and «good» populations are the ones with large values of this parameter. The selection rule is given and its performance is investigated. An application is presented. Similar results for the scale parameters of two gamma populations can be found in van der Laan and van Eeden (1996).

U2 - 10.1007/BF03178926

DO - 10.1007/BF03178926

M3 - Article

VL - 7

SP - 159

EP - 170

JO - Journal of the Italian Statistical Society

JF - Journal of the Italian Statistical Society

SN - 1121-9130

IS - 2

ER -