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Autor:
O'Neill, Meadhbh, Burke, Kevin
Datasets with extreme observations and/or heavy-tailed error distributions are commonly encountered and should be analyzed with careful consideration of these features from a statistical perspective. Small deviations from an assumed model, such as th
Externí odkaz:
http://arxiv.org/abs/2212.07317
Publikováno v:
2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI) (2021) 659-664
Process visualizations of data from manufacturing execution systems (MESs) provide the ability to generate valuable insights for improved decision-making. Industry 4.0 is awakening a digital transformation where advanced analytics and visualizations
Externí odkaz:
http://arxiv.org/abs/2201.06465
Autor:
O'Neill, Meadhbh, Burke, Kevin
Modern variable selection procedures make use of penalization methods to execute simultaneous model selection and estimation. A popular method is the LASSO (least absolute shrinkage and selection operator), the use of which requires selecting the val
Externí odkaz:
http://arxiv.org/abs/2110.02643
Akademický článek
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Autor:
O’Neill, Meadhbh, Burke, Kevin
Publikováno v:
Statistics & Computing; Jun2023, Vol. 33 Issue 3, p1-16, 16p
Autor:
O'Neill, Meadhbh, Burke, Kevin
Modern variable selection procedures make use of penalization methods to execute simultaneous model selection and estimation. A popular method is the LASSO (least absolute shrinkage and selection operator), which contains a tuning parameter. This par
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7cf7a47e46f391829f6b122a466feb35