Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Nipoti, Bernardo"'
The increasing availability of multiple network data has highlighted the need for statistical models for heterogeneous populations of networks. A convenient framework makes use of metrics to measure similarity between networks. In this context, we pr
Externí odkaz:
http://arxiv.org/abs/2410.10354
Motivated by an increasing demand for models that can effectively describe features of complex multivariate time series, e.g. from sensor data in biomechanics, motion analysis, and sports science, we introduce a novel state-space modeling framework w
Externí odkaz:
http://arxiv.org/abs/2407.20085
Matrix factorization exploits the idea that, in complex high-dimensional data, the actual signal typically lies in lower-dimensional structures. These lower dimensional objects provide useful insight, with interpretability favored by sparse structure
Externí odkaz:
http://arxiv.org/abs/2212.06504
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
The stratified proportional hazards model represents a simple solution to account for heterogeneity within the data while keeping the multiplicative effect on the hazard function. Strata are typically defined a priori by resorting to the values taken
Externí odkaz:
http://arxiv.org/abs/2103.09305
Publikováno v:
Journal of Inequalities and Applications, 2020
We prove a monotonicity property of the Hurwitz zeta function which, in turn, translates into a chain of inequalities for polygamma functions of different orders. We provide a probabilistic interpretation of our result by exploiting a connection betw
Externí odkaz:
http://arxiv.org/abs/1909.07026
Nonparametric mixture models based on the Pitman-Yor process represent a flexible tool for density estimation and clustering. Natural generalization of the popular class of Dirichlet process mixture models, they allow for more robust inference on the
Externí odkaz:
http://arxiv.org/abs/1906.08147
Publikováno v:
In Econometrics and Statistics July 2023 27:120-135
Publikováno v:
Computational Statistics (2020)
Location-scale Dirichlet process mixtures of Gaussians (DPM-G) have proved extremely useful in dealing with density estimation and clustering problems in a wide range of domains. Motivated by an astronomical application, in this work we address the r
Externí odkaz:
http://arxiv.org/abs/1809.02463
Publikováno v:
In Computational Statistics and Data Analysis December 2022 176