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An introduction to envelope models and methods: dimension reduction for efficient estimation in multivariate statistics

Detalles Bibliográficos
Autor principal: Cook, R Dennis
Lenguaje:eng
Publicado: John Wiley & Sons 2018
Materias:
Acceso en línea:http://cds.cern.ch/record/2651681
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author Cook, R Dennis
author_facet Cook, R Dennis
author_sort Cook, R Dennis
collection CERN
id cern-2651681
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2018
publisher John Wiley & Sons
record_format invenio
spelling cern-26516812021-04-21T18:37:58Zhttp://cds.cern.ch/record/2651681engCook, R DennisAn introduction to envelope models and methods: dimension reduction for efficient estimation in multivariate statisticsMathematical Physics and MathematicsJohn Wiley & Sonsoai:cds.cern.ch:26516812018
spellingShingle Mathematical Physics and Mathematics
Cook, R Dennis
An introduction to envelope models and methods: dimension reduction for efficient estimation in multivariate statistics
title An introduction to envelope models and methods: dimension reduction for efficient estimation in multivariate statistics
title_full An introduction to envelope models and methods: dimension reduction for efficient estimation in multivariate statistics
title_fullStr An introduction to envelope models and methods: dimension reduction for efficient estimation in multivariate statistics
title_full_unstemmed An introduction to envelope models and methods: dimension reduction for efficient estimation in multivariate statistics
title_short An introduction to envelope models and methods: dimension reduction for efficient estimation in multivariate statistics
title_sort introduction to envelope models and methods: dimension reduction for efficient estimation in multivariate statistics
topic Mathematical Physics and Mathematics
url http://cds.cern.ch/record/2651681
work_keys_str_mv AT cookrdennis anintroductiontoenvelopemodelsandmethodsdimensionreductionforefficientestimationinmultivariatestatistics
AT cookrdennis introductiontoenvelopemodelsandmethodsdimensionreductionforefficientestimationinmultivariatestatistics