Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Roberto Rocci"'
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
Publikováno v:
Psychometrika
Factor analysis is a well-known method for describing the covariance structure among a set of manifest variables through a limited number of unobserved factors. When the observed variables are collected at various occasions on the same statistical un
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b72c4f1cbe68c8f2013234c90742a29
http://hdl.handle.net/11573/1471411
http://hdl.handle.net/11573/1471411
We consider an equivariant approach imposing data-driven bounds for the variances to avoid singular and spurious solutions in maximum likelihood estimation of clusterwise linear regression models. We investigate its use in the choice of the number of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3bb5920e44c4994bb7755cacd027e46b
http://hdl.handle.net/11573/1351560
http://hdl.handle.net/11573/1351560