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publications.bib
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@article{
11567_1162440,
author = {Carli, Federico and Pesce, Elena and Porro, Francesco and Riccomagno, Eva},
title = {Combination of autoregressive graphical models and time series bootstrap methods for risk management in marine insurance},
year = {2024},
journal = {SOCIO-ECONOMIC PLANNING SCIENCES},
volume = {92},
doi = {10.1016/j.seps.2024.101833},
pages = {1--13}
}
@article{
11567_1160775,
author = {Ugolini, Alessandro and Porro, Francesco and Carli, Federico and Agostino, Paola and Silvestrini-Biavati, Armando and Riccomagno, Eva},
title = {Probabilistic graphical modelling of early childhood caries development},
year = {2023},
publisher = {PUBLIC LIBRARY SCIENCE},
address = {1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA},
journal = {PLOS ONE},
volume = {18},
keywords = {early childhood caries, undirected graphical models, sugar intake, oral hygiene, caries prevention, caries risk factors},
doi = {10.1371/journal.pone.0293221},
pages = {1--8},
number = {10}
}
@article{
11567_1104435,
author = {Pesce, Elena and Rapallo, Fabio and Riccomagno, Eva and Wynn, Henry P.},
title = {Generation of all randomizations using circuits},
year = {2023},
journal = {ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS},
volume = {75},
keywords = {Algebraic statistics and combinatorics, A/B testing, Bias and confounders, Big data, Design of experiments},
doi = {10.1007/s10463-022-00860-4},
pages = {683--704},
number = {4}
}
@article{
11567_1154756,
author = {Rapetti, R. and Franchino, E. C. and Visca, S. and Riccomagno, E. and Porro, F. and Vittonetto, D. and Piacenza, A.},
title = {Observed and Perceived Pain: Findings of a Cross-Sectional Study in Hospitalized Subjects},
year = {2023},
journal = {PAIN MANAGEMENT NURSING},
doi = {10.1016/j.pmn.2023.09.011},
pages = {1--6}
}
@article{
11567_1091678,
author = {Pesce, Elena and Porro, Francesco and Riccomagno, Eva},
title = {Large datasets, bias and model‐oriented optimal design of experiments},
year = {2023},
journal = {QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL},
volume = {39},
abstract = {We review recent literature that proposes to adapt ideas from classical model based optimal design of experiments to problems of data selection of large datasets. Special attention is given to bias reduction and to protection against confounders. Some new results are presented. Theoretical and computational comparisons are made.},
keywords = {confounders, large datasets, model bias, optimal experimental design},
doi = {10.1002/qre.3165},
pages = {532--545},
number = {2}
}
@article{
11567_1104576,
author = {Rapetti, Roberta and Vittonetto, Debora and Piacenza, Alberto and Visca, Simona and Colmia Franchino, Elena and Pistone, Marina and Luisa Carofiglio, Maria and Manitto, Rebecca and Garra, Luca and Cirone, Monica and Cihrean, Deborah and Riccomagno, Eva},
title = {The patients’ voice with heart failure: results of a pilot study},
year = {2022},
journal = {GIORNALE ITALIANO DI INFERMIERISTICA},
volume = {40},
pages = {84--88}
}
@article{
11567_1096857,
author = {Leonelli, Manuele and Riccomagno, Eva},
title = {A geometric characterization of sensitivity analysis in monomial models},
year = {2022},
journal = {INTERNATIONAL JOURNAL OF APPROXIMATE REASONING},
volume = {151},
abstract = {Sensitivity analysis in probabilistic discrete graphical models is usually conducted by varying one probability at a time and observing how this affects output probabilities of interest. When one probability is varied, then others are proportionally covaried to respect the sum-to-one condition of probabilities. The choice of proportional covariation is justified by multiple optimality conditions, under which the original and the varied distributions are as close as possible under different measures. For variations of more than one parameter at a time and for the large class of discrete statistical models entertaining a regular monomial parametrisation, we demonstrate the optimality of newly defined proportional multi-way schemes with respect to an optimality criterion based on the I-divergence. We demonstrate that there are varying parameters’ choices for which proportional covariation is not optimal and identify the sub-family of distributions where the distance between the original distribution and the one where probabilities are covaried proportionally is minimum. This is shown by adopting a new geometric characterization of sensitivity analysis in monomial models, which include most probabilistic graphical models. We also demonstrate the optimality of proportional covariation for multi-way analyses in Naive Bayes classifiers.},
keywords = {Bayesian network classifiers,
Covariation,
I-projections,
Monomial models,
Sensitivity analysis},
doi = {10.1016/j.ijar.2022.09.006},
pages = {64--84}
}
@article{
11567_1082342,
author = {Carli, Federico and Leonelli, Manuele and Riccomagno, Eva and Varando, Gherardo},
title = {The R Package stagedtrees for Structural Learning of Stratified Staged Trees},
year = {2022},
publisher = {Foundation for Open Access Statistics},
journal = {JOURNAL OF STATISTICAL SOFTWARE},
volume = {102},
keywords = {chain event graphs, graphical models, R, staged trees, structure learning algorithms},
doi = {10.18637/jss.v102.i06},
pages = {1--30},
number = {6}
}
@article{
11567_1010357,
author = {Leonelli, Manuele and Riccomagno, Eva and Smith, Jim Q.},
title = {Coherent combination of probabilistic outputs for group decision making: an algebraic approach},
year = {2020},
publisher = {Springer},
journal = {OR SPECTRUM},
volume = {42 (2)},
keywords = {Bayesian networks, Integrating decision support systems, Polynomial algebra, Structural equation models},
doi = {10.1007/s00291-020-00588-8},
pages = {499--528},
number = {2}
}
@article{
11567_942899,
author = {Fassino, Claudia and Riccomagno, Eva and Rogantin, MARIA PIERA},
title = {Cubature rules and expected value of some complex functions},
year = {2019},
publisher = {Illinois Institute of Technology Department of Applied Mathematics},
address = {Chicago},
journal = {JOURNAL OF ALGEBRAIC STATISTICS},
volume = {10},
keywords = {Design of experiments, Indicator function, Interpolatory cubature formulae, Precision space, Complex functions, Evaluation of expected values},
doi = {10.18409/jas.v10i1.72},
pages = {114--127},
number = {1}
}
@conference{
11567_1030544,
author = {Pesce, Elena and Riccomagno, Eva and Wynn, Henry P.},
title = {Experimental Design Issues in Big Data: The Question of Bias},
year = {2019},
publisher = {Springer},
booktitle = {Statistical Learning of Complex Data},
abstract = {Data can be collected in scientific studies via a controlled experiment or
passive observation. Big data is often collected in a passive way, e.g. from social
media. In studies of causation great efforts are made to guard against bias and
hidden confounders or feedback which can destroy the identification of causation
by corrupting or omitting counterfactuals (controls). Various solutions of these
problems are discussed, including randomisation.},
keywords = {Big data, Model bias, Experimental design, Nash equilibrium},
doi = {10.1007/978-3-030-21140-0_20},
isbn = {978-3-030-21139-4},
isbn = {978-3-030-21140-0},
pages = {193--200}
}
@conference{
11567_953415,
author = {Barani, S. and Mascandola, C. and Riccomagno, E. and Spallarossa, D. and Albarello, D. and Ferretti, G. and Scafidi, D. and Massa, P. Augliera and M.},
title = {Long-Range Dependence in the Seismic Process and Implications for Earthquake Forecasting},
year = {2018},
booktitle = {Book of Abstracts},
pages = {N/A--N/A}
}
@conference{
11567_882814,
author = {Fassino, Claudia and Hans Michael, Moeller and Riccomagno, Eva},
title = {BM algorithms for noisy data and implicit regression modelling},
year = {2018},
publisher = {Mathematical Society of Japan},
journal = {ADVANCED STUDIES IN PURE MATHEMATICS},
booktitle = {The 50th Anniversary of Gröbner Bases (Advanced Studies in Pure Mathematics, vol 77, 2018 )},
keywords = {zero dimensional varieties, noisy data},
doi = {10.2969/aspm/07710087},
isbn = {978-4-86497-052-5},
pages = {87--107}
}
@article{
11567_895451,
author = {Görgen, Christiane and Bigatti, Anna and Riccomagno, Eva and Smith, Jim Q.},
title = {Discovery of statistical equivalence classes using computer algebra},
year = {2018},
publisher = {Elsevier Inc.},
journal = {INTERNATIONAL JOURNAL OF APPROXIMATE REASONING},
volume = {95},
keywords = {Algebraic statistics; Computer algebra; Graphical models; Ideal decomposition; Staged tree models; Software; Theoretical Computer Science; Artificial Intelligence; Applied Mathematics},
doi = {10.1016/j.ijar.2018.01.003},
pages = {167--184}
}
@article{
11567_898598,
author = {Barani, Simone and Mascandola, Claudia and Riccomagno, Eva and Spallarossa, Daniele and Albarello, Dario and Ferretti, Gabriele and Scafidi, Davide and Augliera, Paolo and Massa, Marco},
title = {Long-range dependence in earthquake-moment release and implications for earthquake occurrence probability},
year = {2018},
publisher = {Nature Publishing Group},
address = {Londra},
journal = {SCIENTIFIC REPORTS},
volume = {8, fasc. 1},
doi = {10.1038/s41598-018-23709-4},
pages = {1--11},
number = {1}
}