Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Semi-parametric mixtures"'
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Skhosana, Sphiwe B.1 (AUTHOR) spiwe.skhosana@up.ac.za, Millard, Salomon M.1 (AUTHOR), Kanfer, Frans H. J.1 (AUTHOR)
Publikováno v:
Mathematics (2227-7390). Mar2023, Vol. 11 Issue 5, p1087. 20p.
Publikováno v:
Mathematics, Vol 11, Iss 5, p 1087 (2023)
Semi- and non-parametric mixture of normal regression models are a flexible class of mixture of regression models. These models assume that the component mixing proportions, regression functions and/or variances are non-parametric functions of the co
Externí odkaz:
https://doaj.org/article/c85b3aa96bc640ee986b70b4d4d3de89
Autor:
Wang, Yong1, Chee, Chew-Seng2
Publikováno v:
Statistical Modelling: An International Journal. Mar2012, Vol. 12 Issue 1, p67-92. 26p. 5 Charts, 7 Graphs.
Publikováno v:
Mathematics
Volume 11
Issue 5
Pages: 1087
Volume 11
Issue 5
Pages: 1087
Semi- and non-parametric mixture of normal regression models are a flexible class of mixture of regression models. These models assume that the component mixing proportions, regression functions and/or variances are non-parametric functions of the co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8d4e6f25717c2f0cac2d8d6e4fea586
https://doi.org/10.3390/math11051087
https://doi.org/10.3390/math11051087
Autor:
Yong Wang, Chew-Seng Chee
Publikováno v:
Statistical Modelling. 12:67-92
This article presents a general framework for univariate non-parametric density estimation, based on mixture models. Similar to kernel-based estimation, the proposed approach uses bandwidth to control the density smoothness, but each density estimate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea89647adc2551e3e438edb8491c28e3
https://doi.org/10.1177/1471082x1001200104
https://doi.org/10.1177/1471082x1001200104
Publikováno v:
The Annals of Statistics, 2007 Feb 01. 35(1), 224-251.
Externí odkaz:
https://www.jstor.org/stable/25463554
Autor:
Laurent Bordes, Didier Chauveau
Publikováno v:
Computational Statistics
Computational Statistics, Springer Verlag, 2016, 31 (4), pp.1513-1538. ⟨10.1007/s00180-016-0661-7⟩
Computational Statistics, Springer Verlag, 2016, 31 (4), pp.1513-1538. ⟨10.1007/s00180-016-0661-7⟩
International audience; Mixture models in reliability bring a useful compromise between parametric and nonparametric models, when several failure modes are suspected. The classical methods for estimation in mixture models rarely handle the additional
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21c8a30c242ef0024a228656e3508adf
https://hal.archives-ouvertes.fr/hal-01458924
https://hal.archives-ouvertes.fr/hal-01458924
Autor:
Dominique Arrouays, Didier Chauveau, Thomas G. Orton, Nicolas Saby, Christian Walter, Blandine Lemercier
Publikováno v:
Geoderma
Geoderma, Elsevier, 2014, 219-220, pp.117-124. ⟨10.1016/j.geoderma.2013.12.016⟩
Geoderma, Elsevier, 2014, 219-220, pp.117-124. ⟨10.1016/j.geoderma.2013.12.016⟩
International audience; Investigating the information of the French National Soil Tests database for soil monitoring produces multiple hypothesis testing problems with hundreds or thousands of test responses to consider simultaneously. A largely used
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff6b21df0ad0c9673f6ece17f39e3364
https://hal.archives-ouvertes.fr/hal-00948553
https://hal.archives-ouvertes.fr/hal-00948553
Publikováno v:
Ann. Statist. 35, no. 1 (2007), 224-251
This article discusses the problem of estimation of parameters in finite mixtures when the mixture components are assumed to be symmetric and to come from the same location family. We refer to these mixtures as semi-parametric because no additional a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5baa93e604fe0236cbc57716516ff80c
https://projecteuclid.org/euclid.aos/1181100187
https://projecteuclid.org/euclid.aos/1181100187