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Conditionally unbiased estimation in the normal setting with unknown variances
To efficiently and completely correct for selection bias in adaptive two-stage trials, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been derived for trial designs with normally distributed data. However, a common assumption is that the variances are known exactly, whic...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540744/ https://www.ncbi.nlm.nih.gov/pubmed/31217751 http://dx.doi.org/10.1080/03610926.2017.1417429 |
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author | Robertson, David S. Glimm, Ekkehard |
author_facet | Robertson, David S. Glimm, Ekkehard |
author_sort | Robertson, David S. |
collection | PubMed |
description | To efficiently and completely correct for selection bias in adaptive two-stage trials, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been derived for trial designs with normally distributed data. However, a common assumption is that the variances are known exactly, which is unlikely to be the case in practice. We extend the work of Cohen and Sackrowitz (Statistics & Probability Letters, 8(3):273-278, 1989), who proposed an UMVCUE for the best performing candidate in the normal setting with a common unknown variance. Our extension allows for multiple selected candidates, as well as unequal stage one and two sample sizes. |
format | Online Article Text |
id | pubmed-6540744 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-65407442019-06-17 Conditionally unbiased estimation in the normal setting with unknown variances Robertson, David S. Glimm, Ekkehard Commun Stat Theory Methods Original Articles To efficiently and completely correct for selection bias in adaptive two-stage trials, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been derived for trial designs with normally distributed data. However, a common assumption is that the variances are known exactly, which is unlikely to be the case in practice. We extend the work of Cohen and Sackrowitz (Statistics & Probability Letters, 8(3):273-278, 1989), who proposed an UMVCUE for the best performing candidate in the normal setting with a common unknown variance. Our extension allows for multiple selected candidates, as well as unequal stage one and two sample sizes. Taylor & Francis 2018-01-05 /pmc/articles/PMC6540744/ /pubmed/31217751 http://dx.doi.org/10.1080/03610926.2017.1417429 Text en © The Author(s). Published with license by Taylor & Francis Group, LLC http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The moral rights of the named author(s) have been asserted. |
spellingShingle | Original Articles Robertson, David S. Glimm, Ekkehard Conditionally unbiased estimation in the normal setting with unknown variances |
title | Conditionally unbiased estimation in the normal setting with unknown variances |
title_full | Conditionally unbiased estimation in the normal setting with unknown variances |
title_fullStr | Conditionally unbiased estimation in the normal setting with unknown variances |
title_full_unstemmed | Conditionally unbiased estimation in the normal setting with unknown variances |
title_short | Conditionally unbiased estimation in the normal setting with unknown variances |
title_sort | conditionally unbiased estimation in the normal setting with unknown variances |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6540744/ https://www.ncbi.nlm.nih.gov/pubmed/31217751 http://dx.doi.org/10.1080/03610926.2017.1417429 |
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