Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Cira Perna"'
Autor:
Michele La Rocca, Cira Perna
Publikováno v:
Stats, Vol 5, Iss 2, Pp 440-457 (2022)
Artificial neural networks are powerful tools for data analysis, particularly in the context of highly nonlinear regression models. However, their utility is critically limited due to the lack of interpretation of the model given its black-box nature
Externí odkaz:
https://doaj.org/article/30bd076100934ec5915dc7914bc2d8ef
Autor:
Cira Perna, Marilena Sibillo
Publikováno v:
Computation, Vol 11, Iss 4, p 80 (2023)
Comparison and cultural exchange always enrich and produce innovative and interesting results [...]
Externí odkaz:
https://doaj.org/article/0e93fb4f465d446392b5e8c866610090
Autor:
Michele La Rocca, Cira Perna
Publikováno v:
Mathematical Biosciences and Engineering, Vol 17, Iss 1, Pp 636-653 (2020)
The aim of the paper is to propose and discuss a sieve bootstrap scheme based on Extreme Learning Machines for non linear time series. The procedure is fully nonparametric in its spirit and retains the conceptual simplicity of the residual bootstrap.
Externí odkaz:
https://doaj.org/article/aa1e59c477c648179a9ab9e70e866b51
Publikováno v:
International Journal of Approximate Reasoning. 137:1-15
A new method for clustering nonlinear time series data is proposed. It is based on the forecast distributions, which are estimated by using a feed-forward neural network and the pair bootstrap. The procedure is shown to deliver consistent results for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::80682de227e10d3959248f6b03b6207f
https://doi.org/10.1016/j.ijar.2021.06.014
https://doi.org/10.1016/j.ijar.2021.06.014
Publikováno v:
Computational Statistics. 36:2917-2938
Missing data reconstruction is a critical step in the analysis and mining of spatio-temporal data. However, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. To take into account the uncer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd6c776280210c0d18a88bb4fc5f1e6f
https://doi.org/10.1007/s00180-021-01099-y
https://doi.org/10.1007/s00180-021-01099-y
Autor:
Cira Perna, Michele La Rocca
Publikováno v:
Mathematical Biosciences and Engineering, Vol 17, Iss 1, Pp 636-653 (2020)
The aim of the paper is to propose and discuss a sieve bootstrap scheme based on Extreme Learning Machines for non linear time series. The procedure is fully nonparametric in its spirit and retains the conceptual simplicity of the residual bootstrap.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::372c5311fc98acbd74bb17c9499e6f31
https://doi.org/10.3934/mbe.2020033
https://doi.org/10.3934/mbe.2020033
Autor:
Michele La Rocca, Cira Perna
Publikováno v:
Mathematical and Statistical Methods for Actuarial Sciences and Finance ISBN: 9783030996376
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56db7d4f57c72266db1ec3a74e817874
http://hdl.handle.net/11386/4783126
http://hdl.handle.net/11386/4783126
A Comparison Among Alternative Parameters Estimators in the Vasicek Process: A Small Sample Analysis
Publikováno v:
Mathematical and Statistical Methods for Actuarial Sciences and Finance ISBN: 9783030789640
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1f815e208f953b6c30f0ed2d21cc12f
https://doi.org/10.1007/978-3-030-78965-7_1
https://doi.org/10.1007/978-3-030-78965-7_1