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Article: Parametric embedding of nonparametric inference problems

TitleParametric embedding of nonparametric inference problems
Authors
KeywordsCensored and truncated data
Hazard rank tests
Nonparametric inference
Parametric embedding
Smooth tests
Issue Date2018
PublisherSpringer New York LLC. The Journal's web site is located at https://www.springer.com/statistics/journal/42519
Citation
Journal of Statistical Theory and Practice, 2018, v. 12 n. 1, p. 151-164 How to Cite?
AbstractIn 1937, Neyman introduced the notion of smooth tests of the null hypothesis that the sample data come from a uniform distribution on the interval (0,1) against alternatives in a smooth parametric family. This idea can be used to embed various nonparametric inference problems in a parametric family. Focusing on nonparametric rank tests, we show how to derive traditional rank tests by applying this approach. We also show how to use it to obtain simplifying insights and optimality results in complicated settings that involve censored and truncated data, for which it is more convenient to use hazard functions to define the embedded family. We describe an application of the embedding approach to the problem of testing for trend in environmental studies.
Persistent Identifierhttp://hdl.handle.net/10722/260590
ISSN
2023 Impact Factor: 0.6
2023 SCImago Journal Rankings: 0.312
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorAlvo, M-
dc.contributor.authorLai, TL-
dc.contributor.authorYu, PLH-
dc.date.accessioned2018-09-14T08:44:10Z-
dc.date.available2018-09-14T08:44:10Z-
dc.date.issued2018-
dc.identifier.citationJournal of Statistical Theory and Practice, 2018, v. 12 n. 1, p. 151-164-
dc.identifier.issn1559-8608-
dc.identifier.urihttp://hdl.handle.net/10722/260590-
dc.description.abstractIn 1937, Neyman introduced the notion of smooth tests of the null hypothesis that the sample data come from a uniform distribution on the interval (0,1) against alternatives in a smooth parametric family. This idea can be used to embed various nonparametric inference problems in a parametric family. Focusing on nonparametric rank tests, we show how to derive traditional rank tests by applying this approach. We also show how to use it to obtain simplifying insights and optimality results in complicated settings that involve censored and truncated data, for which it is more convenient to use hazard functions to define the embedded family. We describe an application of the embedding approach to the problem of testing for trend in environmental studies.-
dc.languageeng-
dc.publisherSpringer New York LLC. The Journal's web site is located at https://www.springer.com/statistics/journal/42519-
dc.relation.ispartofJournal of Statistical Theory and Practice-
dc.rightsThe final publication is available at Springer via http://dx.doi.org/[insert DOI]-
dc.subjectCensored and truncated data-
dc.subjectHazard rank tests-
dc.subjectNonparametric inference-
dc.subjectParametric embedding-
dc.subjectSmooth tests-
dc.titleParametric embedding of nonparametric inference problems-
dc.typeArticle-
dc.identifier.emailYu, PLH: plhyu@hku.hk-
dc.identifier.authorityYu, PLH=rp00835-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/15598608.2017.1399840-
dc.identifier.scopuseid_2-s2.0-85036507900-
dc.identifier.hkuros290948-
dc.identifier.volume12-
dc.identifier.issue1-
dc.identifier.spage151-
dc.identifier.epage164-
dc.identifier.isiWOS:000435858600013-
dc.publisher.placeUnited States-
dc.identifier.issnl1559-8608-

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