Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics

We consider maximum likelihood estimation with data from a bivariate Gaussian process with a separable exponential covariance model under fixed domain asymptotics. We first characterize the equivalence of Gaussian measures under this model. Then consistency and asymptotic normality for the maximum l...

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Autores:
Velandia Munoz, Daira Luz
Bachoc, François
Bevilacqua, Moreno
Gendre, Xavier
Loubes, Jean Michel
Tipo de recurso:
Article of journal
Fecha de publicación:
2017
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/1878
Acceso en línea:
https://hdl.handle.net/11323/1878
https://doi.org/10.1214/17-EJS1298
https://repositorio.cuc.edu.co/
Palabra clave:
Bivariate exponential model
Equivalent Gaussian measures
Infill asymptotics
Microergodic parameters
Rights
openAccess
License
Atribución – No comercial – Compartir igual
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dc.title.eng.fl_str_mv Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
title Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
spellingShingle Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
Bivariate exponential model
Equivalent Gaussian measures
Infill asymptotics
Microergodic parameters
title_short Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
title_full Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
title_fullStr Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
title_full_unstemmed Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
title_sort Maximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptotics
dc.creator.fl_str_mv Velandia Munoz, Daira Luz
Bachoc, François
Bevilacqua, Moreno
Gendre, Xavier
Loubes, Jean Michel
dc.contributor.author.spa.fl_str_mv Velandia Munoz, Daira Luz
Bachoc, François
Bevilacqua, Moreno
Gendre, Xavier
Loubes, Jean Michel
dc.subject.eng.fl_str_mv Bivariate exponential model
Equivalent Gaussian measures
Infill asymptotics
Microergodic parameters
topic Bivariate exponential model
Equivalent Gaussian measures
Infill asymptotics
Microergodic parameters
description We consider maximum likelihood estimation with data from a bivariate Gaussian process with a separable exponential covariance model under fixed domain asymptotics. We first characterize the equivalence of Gaussian measures under this model. Then consistency and asymptotic normality for the maximum likelihood estimator of the microergodic parameters are established. A simulation study is presented in order to compare the finite sample behavior of the maximum likelihood estimator with the given asymptotic distribution.
publishDate 2017
dc.date.issued.none.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2018-11-26T19:07:58Z
dc.date.available.none.fl_str_mv 2018-11-26T19:07:58Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.issn.spa.fl_str_mv 19357524
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/1878
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1214/17-EJS1298
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 19357524
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/1878
https://doi.org/10.1214/17-EJS1298
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.rights.spa.fl_str_mv Atribución – No comercial – Compartir igual
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
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rights_invalid_str_mv Atribución – No comercial – Compartir igual
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.spa.fl_str_mv Electronic Journal of Statistics
institution Corporación Universidad de la Costa
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spelling Velandia Munoz, Daira LuzBachoc, FrançoisBevilacqua, MorenoGendre, XavierLoubes, Jean Michel2018-11-26T19:07:58Z2018-11-26T19:07:58Z201719357524https://hdl.handle.net/11323/1878https://doi.org/10.1214/17-EJS1298Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/We consider maximum likelihood estimation with data from a bivariate Gaussian process with a separable exponential covariance model under fixed domain asymptotics. We first characterize the equivalence of Gaussian measures under this model. Then consistency and asymptotic normality for the maximum likelihood estimator of the microergodic parameters are established. A simulation study is presented in order to compare the finite sample behavior of the maximum likelihood estimator with the given asymptotic distribution.Velandia Munoz, Daira Luz-73dfd698-6d37-40e6-ada6-9ba91893daac-0Bachoc, François-c26fdd64-25c5-4a87-bffe-c282ad7f7700-0Bevilacqua, Moreno-3efd5afe-5bf5-4fbf-9d5c-449a98a644e6-0Gendre, Xavier-83ddab22-a6d7-40a0-b296-565ba8c48e6c-0Loubes, Jean Michel-56014099-5667-43ad-a5bd-c5ed5078a92e-0engElectronic Journal of StatisticsAtribución – No comercial – Compartir igualinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Bivariate exponential modelEquivalent Gaussian measuresInfill asymptoticsMicroergodic parametersMaximum likelihood estimation for a bivariate Gaussian process under fixed domain asymptoticsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALMaximum likelihood estimation for a.pdfMaximum likelihood estimation for a.pdfapplication/pdf388808https://repositorio.cuc.edu.co/bitstreams/1f54725f-33fa-40b5-bcbc-796d1d25ad06/downloadaa20997b6452d7f02c818e423e429112MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/74b44a82-3003-4d93-a2d3-d7632011c0cb/download8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILMaximum likelihood estimation for a.pdf.jpgMaximum likelihood estimation for a.pdf.jpgimage/jpeg37743https://repositorio.cuc.edu.co/bitstreams/17f28290-c280-4c73-8fdc-8455d0574626/downloadb39fe00cf41a8d3ba3978ed71b6281ccMD54TEXTMaximum likelihood estimation for a.pdf.txtMaximum likelihood estimation for a.pdf.txttext/plain68921https://repositorio.cuc.edu.co/bitstreams/d343efc4-b5d3-46b5-97ee-fe2a0b532c7d/download07c08cd235bd68f0a4817d353f976837MD5511323/1878oai:repositorio.cuc.edu.co:11323/18782024-09-17 14:23:10.094open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo=

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