Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Migon, Hélio S."'
This paper introduces kDGLM, an R package designed for Bayesian analysis of Generalized Dynamic Linear Models (GDLM), with a primary focus on both uni- and multivariate exponential families. Emphasizing sequential inference for time series data, the
Externí odkaz:
http://arxiv.org/abs/2403.13069
Autor:
Garcia, Nancy L., Rodrigues-Motta, Mariana, Migon, Helio S., Petkova, Eva, Tarpey, Thaddeus, Ogden, R. Todd, Giodano, Julio O., Perez, Martin Matias
Publikováno v:
Journal of the Royal Statistical Society -- Serie C -- Applied Statistics 2024
We consider unsupervised classification by means of a latent multinomial variable which categorizes a scalar response into one of L components of a mixture model. This process can be thought as a hierarchical model with first level modelling a scalar
Externí odkaz:
http://arxiv.org/abs/2202.04037
This paper introduces a novel sequential inferential approach for Bayesian dynamic generalized linear models, focusing on uni- or multivariate k-parametric exponential families, which handle various responses such as multinomial, gamma, normal, and P
Externí odkaz:
http://arxiv.org/abs/2201.05387
This paper proposes a generalization of Gaussian mixture models, where the mixture weight is allowed to behave as an unknown function of time. This model is capable of successfully capturing the features of the data, as demonstrated by simulated and
Externí odkaz:
http://arxiv.org/abs/2104.03395
We develop a fully automatic Bayesian Lasso via variational inference. This is a scalable procedure for approximating the posterior distribution. Special attention is driven to the knot selection in regression spline. In order to carry through our pr
Externí odkaz:
http://arxiv.org/abs/2102.13548
This paper proposes an alternative approach for constructing invariant Jeffreys prior distributions tailored for hierarchical or multilevel models. In particular, our proposal is based on a flexible decomposition of the Fisher information for hierarc
Externí odkaz:
http://arxiv.org/abs/1904.11609
Autor:
Albani, Roseane A.S., Albani, Vinicius V.L., Gomes, Luiz E.S., Migon, Helio S., Silva Neto, Antonio J.
Publikováno v:
In Environmental Pollution 15 March 2023 321
A new class of models, named dynamic quantile linear models, is presented. It combines dynamic linear models with distribution free quantile regression producing a robust statistical method. Bayesian inference for dynamic quantile linear models can b
Externí odkaz:
http://arxiv.org/abs/1711.00162
Publikováno v:
In Environmental Pollution 1 December 2021 290
The Brazilian Mathematical Olympiads for Public Schools (OBMEP) is held every year since 2005. In the 2013 edition there were over 47,000 schools registered involving nearly 19.2 million students. The Brazilian public educational system is structured
Externí odkaz:
http://arxiv.org/abs/1507.00565