Zobrazeno 1 - 10
of 401
pro vyhledávání: '"Argiento, P"'
Statistical modelling in the presence of data organized in groups is a crucial task in Bayesian statistics. The present paper conceives a mixture model based on a novel family of Bayesian priors designed for multilevel data and obtained by normalizin
Externí odkaz:
http://arxiv.org/abs/2310.20376
Crowdsourced smartphone-based earthquake early warning systems recently emerged as reliable alternatives to the more expensive solutions based on scientific-grade instruments. For instance, during the 2023 Turkish-Syrian deadly event, the system impl
Externí odkaz:
http://arxiv.org/abs/2303.00806
The study of almost surely discrete random probability measures is an active line of research in Bayesian nonparametrics. The idea of assuming interaction across the atoms of the random probability measure has recently spurred significant interest in
Externí odkaz:
http://arxiv.org/abs/2302.09034
A model-based approach is developed for clustering categorical data with no natural ordering. The proposed method exploits the Hamming distance to define a family of probability mass functions to model the data. The elements of this family are then c
Externí odkaz:
http://arxiv.org/abs/2212.04746
Motivated by the problem of accurately predicting gap times between successive blood donations, we present here a general class of Bayesian nonparametric models for clustering. These models allow for prediction of new recurrences, accommodating covar
Externí odkaz:
http://arxiv.org/abs/2210.08297
Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patien
Externí odkaz:
http://arxiv.org/abs/2210.06030
Publikováno v:
Journal of Computational and Graphical Statistics 2023
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:
http://arxiv.org/abs/2206.14274
Autor:
Cremaschi, Andrea, Argiento, Raffele, De Iorio, Maria, Shirong, Cai, Chong, Yap Seng, Meaney, Michael J., Kee, Michelle Z. L.
Many applications in medical statistics as well as in other fields can be described by transitions between multiple states (e.g. from health to disease) experienced by individuals over time. In this context, multi-state models are a popular statistic
Externí odkaz:
http://arxiv.org/abs/2106.03072
Autor:
Codazzi, Laura, Colombi, Alessandro, Gianella, Matteo, Argiento, Raffaele, Paci, Lucia, Pini, Alessia
Motivated by the analysis of spectrometric data, we introduce a Gaussian graphical model for learning the dependence structure among frequency bands of the infrared absorbance spectrum. The spectra are modeled as continuous functional data through a
Externí odkaz:
http://arxiv.org/abs/2103.11666
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:
http://arxiv.org/abs/2101.08175