Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Muggeo, V"'
Autor:
Muggeo, V M, Hajat, S
Publikováno v:
Occupational and Environmental Medicine, 2009 Sep 01. 66(9), 584-591.
Externí odkaz:
https://www.jstor.org/stable/27797625
In questo contributo vogliamo discutere di un modello di regressione segmented per valutare e quantificare l’effetto delle misure di contenimento individuando gli istanti temporali in cui la diffusione della malattia rallenta.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3658::54ee7d59e1c31ed391f3d735458044af
http://hdl.handle.net/10447/426725
http://hdl.handle.net/10447/426725
Autor:
Sottile, G, Muggeo, V
Quantile regression can be used to obtain a nonparametric estimate of a conditional quantile function. The presence of quantile crossing, however, leads to an invalid distribution of the response and makes it dicult to use the tted model for predicti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3658::5c6787db5e447d0f63b99d05322e952c
http://hdl.handle.net/10447/426593
http://hdl.handle.net/10447/426593
Autor:
Sottile, G, Muggeo, V
Penalized regression models are popularly used in high-dimensional data analysis to carry out variable selction and model fitting simultaneously. Whereas success has been widely reported in literature, their performance largely depend on the tuning p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3658::ecaf33291ff16599e2dfa24ee09eb3bc
http://hdl.handle.net/10447/419564
http://hdl.handle.net/10447/419564
Autor:
Maio, S., Baldacci, S., Simoni, M., Angino, A., La Grutta, S., Muggeo, V., Fasola, S., Viegi, G., Angino, AGAVE Sudy Group: A., Bresciani, M., Carrozzi, L., Cerrai, S., Martini, F., Pala, A. P., Pistelli, F., Sarno, G., Silvi, P.
Publikováno v:
Journal of Clinical Medicine
Volume 9
Issue 11
Journal of Clinical Medicine, Vol 9, Iss 3632, p 3632 (2020)
Journal of clinical medicine (2020). doi:10.3390/jcm9113632
info:cnr-pdr/source/autori:Sara Maio; Sandra Baldacci; Marzia Simoni; Anna Angino; Stefania La Grutta; Vito Muggeo; Salvatore Fasola; Giovanni Viegi;2 and on behalf of the AGAVE Pisa Group;/titolo:Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors/doi:10.3390%2Fjcm9113632/rivista:Journal of clinical medicine/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume
Volume 9
Issue 11
Journal of Clinical Medicine, Vol 9, Iss 3632, p 3632 (2020)
Journal of clinical medicine (2020). doi:10.3390/jcm9113632
info:cnr-pdr/source/autori:Sara Maio; Sandra Baldacci; Marzia Simoni; Anna Angino; Stefania La Grutta; Vito Muggeo; Salvatore Fasola; Giovanni Viegi;2 and on behalf of the AGAVE Pisa Group;/titolo:Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors/doi:10.3390%2Fjcm9113632/rivista:Journal of clinical medicine/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume
Background: Asthma patterns are not well established in epidemiological studies. Aim: To assess asthma patterns and risk factors in an adult general population sample. Methods: In total, 452 individuals reporting asthma symptoms/diagnosis in previous
Autor:
Maio, S., Fasola, S., Baldacci, S., LAURA CARROZZI, Francesco Pistelli, Simoni, M., La Grutta, S., Muggeo, V., Viegi, Giovanni
Publikováno v:
Università di Pisa-IRIS
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::a9f880bb8c97dab6514a8511ede8a783
http://hdl.handle.net/11568/1125116
http://hdl.handle.net/11568/1125116
Over the past three decades an increasing amount of dating records dealing with Posidonia oceanica growth performance was incorporated into a variety of studies, from which a dualistic nature of the factors influencing seagrass growth arose. A large
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3658::532202d8be8036e0583e53a05507f9c0
http://hdl.handle.net/10447/241576
http://hdl.handle.net/10447/241576
Akademický článek
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Autor:
Sottile, G., Muggeo, V.
We propose a new method to select the tuning parameter in lasso regression. Unlike the previous proposals, the method is iterative and thus it is particularly efficient when multiple tuning parameters have to be selected. The method also applies to m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::e03bb9b3196341094799dec42e78c6ee
http://hdl.handle.net/10447/221666
http://hdl.handle.net/10447/221666
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
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