Zobrazeno 1 - 10
of 892
pro vyhledávání: '"A, Lijoi"'
Multilayer networks generalize single-layered connectivity data in several directions. These generalizations include, among others, settings where multiple types of edges are observed among the same set of nodes (edge-colored networks) or where a sin
Externí odkaz:
http://arxiv.org/abs/2410.10619
Comparing survival experiences of different groups of data is an important issue in several applied problems. A typical example is where one wishes to investigate treatment effects. Here we propose a new Bayesian approach based on restricted mean sur
Externí odkaz:
http://arxiv.org/abs/2407.11614
Modeling of the dependence structure across heterogeneous data is crucial for Bayesian inference since it directly impacts the borrowing of information. Despite the extensive advances over the last two decades, most available proposals allow only for
Externí odkaz:
http://arxiv.org/abs/2310.00617
Publikováno v:
Biometrika, 2022
Dirichlet process mixtures are flexible non-parametric models, particularly suited to density estimation and probabilistic clustering. In this work we study the posterior distribution induced by Dirichlet process mixtures as the sample size increases
Externí odkaz:
http://arxiv.org/abs/2205.12924
We study the distribution of the unobserved states of two measure-valued diffusions of Fleming-Viot and Dawson-Watanabe type, conditional on observations from the underlying populations collected at past, present and future times. If seen as nonparam
Externí odkaz:
http://arxiv.org/abs/2204.12738
Hypertensive disorders of pregnancy occur in about 10% of pregnant women around the world. Though there is evidence that hypertension impacts maternal cardiac functions, the relation between hypertension and cardiac dysfunctions is only partially und
Externí odkaz:
http://arxiv.org/abs/2203.15782
Publikováno v:
Scandinavian Journal of Statistics, 2022
The Bayesian approach to inference stands out for naturally allowing borrowing information across heterogeneous populations, with different samples possibly sharing the same distribution. A popular Bayesian nonparametric model for clustering probabil
Externí odkaz:
http://arxiv.org/abs/2201.06994
Autor:
Arbel, Julyan, King, Guillaume Kon Kam, Lijoi, Antonio, Nieto-Barajas, Luis Enrique, Prünster, Igor
Robust statistical data modelling under potential model mis-specification often requires leaving the parametric world for the nonparametric. In the latter, parameters are infinite dimensional objects such as functions, probability distributions or in
Externí odkaz:
http://arxiv.org/abs/2110.10019
Optimal transport and Wasserstein distances are flourishing in many scientific fields as a means for comparing and connecting random structures. Here we pioneer the use of an optimal transport distance between L\'{e}vy measures to solve a statistical
Externí odkaz:
http://arxiv.org/abs/2109.06646
Discrete Bayesian nonparametric models whose expectation is a convex linear combination of a point mass at some point of the support and a diffuse probability distribution allow to incorporate strong prior information, while still being extremely fle
Externí odkaz:
http://arxiv.org/abs/2107.10223