Equivariance and Generalized Inference in Two-Sample Location-Scale Families

We are interested in-typical Behrens-Fisher problem in general location-scale families. We present a method of constructing generalized pivotal quantity (GPQ) and generalized P value (GPV) for the difference between two location parameters. The suggested method is based on the minimum risk equivaria...

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Main Authors: Sévérien Nkurunziza, Fuqi Chen
Format: Article
Language:English
Published: Wiley 2011-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2011/474826
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author Sévérien Nkurunziza
Fuqi Chen
author_facet Sévérien Nkurunziza
Fuqi Chen
author_sort Sévérien Nkurunziza
collection DOAJ
description We are interested in-typical Behrens-Fisher problem in general location-scale families. We present a method of constructing generalized pivotal quantity (GPQ) and generalized P value (GPV) for the difference between two location parameters. The suggested method is based on the minimum risk equivariant estimators (MREs), and thus, it is an extension of the methods based on maximum likelihood estimators and conditional inference, which have been, so far, applied to some specific distributions. The efficiency of the procedure is illustrated by Monte Carlo simulation studies. Finally, we apply the proposed method to two real datasets.
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publishDate 2011-01-01
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spelling doaj-art-e7ae8b97571b401b889bfd651c96df182025-08-20T03:21:02ZengWileyJournal of Probability and Statistics1687-952X1687-95382011-01-01201110.1155/2011/474826474826Equivariance and Generalized Inference in Two-Sample Location-Scale FamiliesSévérien Nkurunziza0Fuqi Chen1Department of Mathematics and Statistics, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B 3P4, CanadaDepartment of Mathematics and Statistics, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B 3P4, CanadaWe are interested in-typical Behrens-Fisher problem in general location-scale families. We present a method of constructing generalized pivotal quantity (GPQ) and generalized P value (GPV) for the difference between two location parameters. The suggested method is based on the minimum risk equivariant estimators (MREs), and thus, it is an extension of the methods based on maximum likelihood estimators and conditional inference, which have been, so far, applied to some specific distributions. The efficiency of the procedure is illustrated by Monte Carlo simulation studies. Finally, we apply the proposed method to two real datasets.http://dx.doi.org/10.1155/2011/474826
spellingShingle Sévérien Nkurunziza
Fuqi Chen
Equivariance and Generalized Inference in Two-Sample Location-Scale Families
Journal of Probability and Statistics
title Equivariance and Generalized Inference in Two-Sample Location-Scale Families
title_full Equivariance and Generalized Inference in Two-Sample Location-Scale Families
title_fullStr Equivariance and Generalized Inference in Two-Sample Location-Scale Families
title_full_unstemmed Equivariance and Generalized Inference in Two-Sample Location-Scale Families
title_short Equivariance and Generalized Inference in Two-Sample Location-Scale Families
title_sort equivariance and generalized inference in two sample location scale families
url http://dx.doi.org/10.1155/2011/474826
work_keys_str_mv AT severiennkurunziza equivarianceandgeneralizedinferenceintwosamplelocationscalefamilies
AT fuqichen equivarianceandgeneralizedinferenceintwosamplelocationscalefamilies