Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Pietro Muliere"'
Publikováno v:
Algorithms, Vol 14, Iss 1, p 11 (2021)
Bootstrap resampling techniques, introduced by Efron and Rubin, can be presented in a general Bayesian framework, approximating the statistical distribution of a statistical functional ϕ(F), where F is a random distribution function. Efron’s and R
Externí odkaz:
https://doaj.org/article/86352cabe69244c0b66215efcb909be2
Publikováno v:
Algorithms, Vol 14, Iss 11, p 11 (2021)
Algorithms
Volume 14
Issue 1
Algorithms
Volume 14
Issue 1
Bootstrap resampling techniques, introduced by Efron and Rubin, can be presented in a general Bayesian framework, approximating the statistical distribution of a statistical functional ϕ(F), where F is a random distribution function. Efron&rsquo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c2f07520a1c2b5fd3e5b17ce3d56ed3
https://www.mdpi.com/1999-4893/14/1/11
https://www.mdpi.com/1999-4893/14/1/11
Autor:
Andrea Arfè, Pietro Muliere
We introduce a novel procedure to perform Bayesian non-parametric inference with right-censored data, the \emph{beta-Stacy bootstrap}. This approximates the posterior law of summaries of the survival distribution (e.g. the mean survival time). More p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4451957fb9db47c4bb5c2cbc4f329b3f
Publikováno v:
International Journal of Approximate Reasoning. 83:102-117
Parametric specifications in State Space Models (SSMs) are a source of bias in case of mismatch between modeling assumptions and reality. We propose a Bayesian semiparametric SSM that is robust to misspecified emission distributions. The Markovian na
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68974fab83998f1cd6d417d635621020
https://doi.org/10.1016/j.ijar.2017.01.004
https://doi.org/10.1016/j.ijar.2017.01.004
The literature on Bayesian methods for the analysis of discrete-time semi-Markov processes is sparse. In this paper, we introduce the semi-Markov beta-Stacy process, a stochastic process useful for the Bayesian non-parametric analysis of semi-Markov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08ce0ce8a7742f1895b12b4bb501ba20
http://arxiv.org/abs/1812.00260
http://arxiv.org/abs/1812.00260
In this paper we introduce the subdistribution beta-Stacy process, a novel Bayesian nonparametric process prior for subdistribution functions useful for the analysis of competing risks data. In particular, we i) characterize this process from a predi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a7a6aff0ed87e9323e21a209a88e690
http://arxiv.org/abs/1811.12304
http://arxiv.org/abs/1811.12304
Autor:
Pietro Muliere, Pasquale Cirillo
Publikováno v:
Cirillo, Pasquale; Muliere, Pietro (2013). An urn-based Bayesian block bootstrap. Metrika, 76(1), pp. 93-106. Springer 10.1007/s00184-011-0377-1
Block bootstrap has been introduced in the literature for resampling dependent data, i.e. stationary processes. One of the main assumptions in block bootstrapping is that the blocks of observations are exchangeable, i.e. their joint distribution is i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dcdc3c537e057bbc1070ab08f7acff47
http://doc.rero.ch/record/315075/files/184_2011_Article_377.pdf
http://doc.rero.ch/record/315075/files/184_2011_Article_377.pdf
Publikováno v:
Journal of Statistical Planning and Inference
Journal of Statistical Planning and Inference, Elsevier, 2017, 183, pp.1-17. ⟨10.1016/j.jspi.2016.10.004⟩
Journal of Statistical Planning and Inference, 2017, 183, pp.1-17. ⟨10.1016/j.jspi.2016.10.004⟩
Journal of Statistical Planning and Inference, Elsevier, 2017, 183, pp.1-17. ⟨10.1016/j.jspi.2016.10.004⟩
Journal of Statistical Planning and Inference, 2017, 183, pp.1-17. ⟨10.1016/j.jspi.2016.10.004⟩
Many applications in risk analysis require the estimation of the dependence among multivariate maxima, especially in environmental sciences. Such dependence can be described by the Pickands dependence function of the underlying extreme-value copula.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f51cb5455ae61e6232be897530676d4
https://hal.archives-ouvertes.fr/hal-03226744
https://hal.archives-ouvertes.fr/hal-03226744
Publikováno v:
Electron. J. Statist. 11, no. 2 (2017), 3368-3406
Existing Bayesian nonparametric methodologies for bandit problems focus on exact observations, leaving a gap in those bandit applications where censored observations are crucial. We address this gap by extending a Bayesian nonparametric two-armed ban
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83c9754563704e1046c6809b011a271e
http://hdl.handle.net/10281/266153
http://hdl.handle.net/10281/266153
Publikováno v:
Computational Statistics & Data Analysis. 77:300-312
The ROC curve is one of the most common statistical tools useful to assess classifier performance. The selection of the best classifier when ROC curves intersect is quite challenging. A novel approach for model comparisons when ROC curves show inters
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c6c1495807ad03e6e0166f692dcf9eab
https://doi.org/10.1016/j.csda.2014.03.008
https://doi.org/10.1016/j.csda.2014.03.008