Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Raffaele Argiento"'
Publikováno v:
Journal of Computational and Graphical Statistics. :1-21
Within the framework of Gaussian graphical models, a prior distribution for the underlying graph is introduced to induce a block structure in the adjacency matrix of the graph and learning relationships between fixed groups of variables. A novel samp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::55789c39c2c8f4363599c2de28fab02e
https://doi.org/10.1080/10618600.2023.2210184
https://doi.org/10.1080/10618600.2023.2210184
Autor:
Eleni Matechou, Raffaele Argiento
We propose a novel approach for modelling capture-recapture (CR) data on open populations that exhibit temporary emigration, whilst also accounting for individual heterogeneity to allow for differences in visit patterns and capture probabilities betw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::451f7ec9f07a0864ef3888de5694d4c4
https://kar.kent.ac.uk/96926/11/01621459.2022.pdf
https://kar.kent.ac.uk/96926/11/01621459.2022.pdf
Autor:
Laura Codazzi, Alessandro Colombi, Matteo Gianella, Raffaele Argiento, Lucia Paci, Alessia Pini
Publikováno v:
Computational Statistics and Data Analysis (2022)
Motivated by the analysis of spectrometric data, a Gaussian graphical model for learning the dependence structure among frequency bands of the infrared absorbance spectrum is introduced. The spectra are modeled as continuous functional data through a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::086bc48a69aa76c6c457cd7e27817674
The use of statistical methods in sport analytics has gained a rapidly growing interest over the last decade, and nowadays is common practice. In particular, the interest in understanding and predicting an athlete's performance throughout his/her car
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abcaca8afec15dedddfd997871855899
In this article, we propose a Bayesian nonparametric model for clustering grouped data. We adopt a hierarchical approach: at the highest level, each group of data is modeled according to a mixture, where the mixing distributions are conditionally ind
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cd128eb394a9897480bfa471466a824e
http://hdl.handle.net/10446/193473
http://hdl.handle.net/10446/193473
In professional tennis, it is often acknowledged that the server has an initial advantage. Indeed, the majority of points are won by the server, making the serve one of the most important elements in this sport. In this paper, we focus on the role of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::10bf8e985684b12ea03a7e0594e8fcdc
http://hdl.handle.net/10807/163432
http://hdl.handle.net/10807/163432
Autor:
Rosangela H. Loschi, Raffaele Argiento, Renato M. Assunção, Fabrizio Ruggeri, Márcia D. Branco, Guilherme Lopes de Oliveira
Publikováno v:
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
Data quality from poor and socially deprived regions have given rise to many statistical challenges. One of them is the underreporting of vital events leading to biased estimates for the associated risks. To deal with underreported count data, models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::624f5288b33f3d82cf1bd73d9423ac81
http://hdl.handle.net/10807/163433
http://hdl.handle.net/10807/163433
Publikováno v:
Beraha, M, Argiento, R, Møller, J & Guglielmi, A 2020, ' MCMC computations for Bayesian mixture models using repulsive point processes. ', arXiv.org (e-prints) . < https://arxiv.org/abs/2011.06444 >
Aalborg University
Aalborg University
Repulsive mixture models have recently gained popularity for Bayesian cluster detection. Compared to more traditional mixture models, repulsive mixture models produce a smaller number of well-separated clusters. The most commonly used methods for pos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c6aa74a7584f7017d628c0cef07adaf
This book presents a selection of peer-reviewed contributions to the fifth Bayesian Young Statisticians Meeting, BaYSM 2021, held virtually due to the COVID-19 pandemic on 1-3 September 2021. Despite all the challenges of an online conference, the me
Publikováno v:
Statistical methods & applications 27 (2018): 231–238. doi:10.1007/s10260-017-0397-8
info:cnr-pdr/source/autori:R. Argiento and M. Ruggiero/titolo:Computational challenges and temporal dependence in Bayesian nonparametric models/doi:10.1007%2Fs10260-017-0397-8/rivista:Statistical methods & applications/anno:2018/pagina_da:231/pagina_a:238/intervallo_pagine:231–238/volume:27
info:cnr-pdr/source/autori:R. Argiento and M. Ruggiero/titolo:Computational challenges and temporal dependence in Bayesian nonparametric models/doi:10.1007%2Fs10260-017-0397-8/rivista:Statistical methods & applications/anno:2018/pagina_da:231/pagina_a:238/intervallo_pagine:231–238/volume:27
Müller et al. (Stat Methods Appl, 2017) provide an excellent review of several classes of Bayesian nonparametric models which have found widespread application in a variety of contexts, successfully highlighting their flexibility in comparison with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::324ddb2f0e835b6e250878e0e54c8c5a
https://doi.org/10.1007/s10260-017-0397-8
https://doi.org/10.1007/s10260-017-0397-8