Zobrazeno 1 - 10
of 190
pro vyhledávání: '"Roberto Rocci"'
Autor:
Heggeseth, Brianna C.
Publikováno v:
Journal of the American Statistical Association, 2017 Jun 01. 112(518), 880-881.
Externí odkaz:
https://www.jstor.org/stable/45028417
Autor:
Durante, Fabrizio
Publikováno v:
International Statistical Review / Revue Internationale de Statistique, 2013 Dec 01. 81(3), 460-461.
Externí odkaz:
https://www.jstor.org/stable/43299649
Autor:
Durante, Fabrizio1 fabrizio.durante@unibz.it
Publikováno v:
International Statistical Review. Dec2013, Vol. 81 Issue 3, p460-461. 2p.
Autor:
Fabrizio Durante
Publikováno v:
International Statistical Review. 81:460-461
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7fed8fe8b2b30fa574f6d19e2c3d8429
https://doi.org/10.1111/insr.12042_2
https://doi.org/10.1111/insr.12042_2
Publikováno v:
Computation, Vol 11, Iss 2, p 29 (2023)
The objective of the present paper is to propose a new method to measure the recovery performance of a portfolio of non-performing loans (NPLs) in terms of recovery rate and time to liquidate. The fundamental idea is to draw a curve representing the
Externí odkaz:
https://doaj.org/article/7abb2e21a5514b06a75476694857d935
Autor:
Monia Ranalli, Roberto Rocci
Publikováno v:
Advances in Data Analysis and Classification.
In this paper, we propose twelve parsimonious models for clustering mixed-type (ordinal and continuous) data. The dependence among the different types of variables is modeled by assuming that ordinal and continuous data follow a multivariate finite m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cdcc7231c3df40455db72ac59956753b
https://doi.org/10.1007/s11634-023-00539-5
https://doi.org/10.1007/s11634-023-00539-5
Publikováno v:
Building Bridges between Soft and Statistical Methodologies for Data Science ISBN: 9783031155086
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::36811301545ecc969fd485f762eca23b
http://hdl.handle.net/20.500.11769/536157
http://hdl.handle.net/20.500.11769/536157
Publikováno v:
SSRN Electronic Journal.
In clusterwise regression analysis, the goal is to predict a response variable based on a set of explanatory variables, each with cluster-specific effects. Nowadays, the number of candidates is typically large: whereas some of these variables might b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2688af5da0ee7888195a9043c54b8d67
https://doi.org/10.2139/ssrn.3832769
https://doi.org/10.2139/ssrn.3832769
Autor:
Roberto Rocci, Monia Ranalli
Publikováno v:
Statistical Learning and Modeling in Data Analysis ISBN: 9783030699437
In this paper, we compare through a simulation study two approaches to cluster mixed-type data, where some variables are continuous and some others ordinal. The first is model-based, according to which the variables are assumed to follow a Gaussian m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::123143210021e903c1ffc84f29caae18
https://doi.org/10.1007/978-3-030-69944-4_17
https://doi.org/10.1007/978-3-030-69944-4_17
Publikováno v:
Statistical Learning and Modeling in Data Analysis ISBN: 9783030699437
Several approaches exist to avoid singular and spurious solutions in maximum likelihood (ML) estimation of clusterwise linear regression models. We propose to solve the degeneracy problem by using a penalized approach: this is done by adding a penalt
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8f6bf2339e32e6c953bfba8220debad
http://hdl.handle.net/11573/1603298
http://hdl.handle.net/11573/1603298