Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Helio S. Migon"'
Publikováno v:
Revstat Statistical Journal, Vol 14, Iss 1 (2016)
We develop a hierarchical dynamic Bayesian beta model for modelling a set of time series of rates or proportions. The proposed methodology enables to combine the information contained in different time series so that we can describe a common underlyi
Externí odkaz:
https://doaj.org/article/7beda866aa6a4803862c87cfea62a0ff
Publikováno v:
Applied Stochastic Models in Business and Industry.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e2df457214c2be46c37390e6c2f66dfc
https://doi.org/10.1002/asmb.2756
https://doi.org/10.1002/asmb.2756
Publikováno v:
Brazilian Review of Econometrics. 40:347-373
This work investigates the effects of using the independent Jeffreys prior for the degrees of freedom parameter of a t-student model in the asymmetric generalised autoregressive conditional heteroskedasticity (GARCH) model. To capture asymmetry in th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6023de57c87957ea457ad406c0aee5d2
https://doi.org/10.12660/bre.v40n22020.80292
https://doi.org/10.12660/bre.v40n22020.80292
Autor:
Roseane A.S. Albani, Vinicius V.L. Albani, Luiz E.S. Gomes, Helio S. Migon, Antonio J. Silva Neto
Publikováno v:
Environmental Pollution. 321:121061
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b81ff3e74c02caea23232ad1dd684627
https://doi.org/10.1016/j.envpol.2023.121061
https://doi.org/10.1016/j.envpol.2023.121061
Publikováno v:
Bayesian Anal. 15, no. 2 (2020), 335-362
arXiv
arXiv
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0558e7a759b1fdc56f769a4060c346b8
https://projecteuclid.org/euclid.ba/1556244057
https://projecteuclid.org/euclid.ba/1556244057
Publikováno v:
Computational Statistics & Data Analysis. 121:164-179
A Bayesian framework for estimation and prediction of dynamic models for observations from the two-parameter exponential family is developed. Different link functions are introduced to model both the mean and the precision in the exponential family a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b0d27cd97dea830357d8c6271db8bb9
https://doi.org/10.1016/j.csda.2016.09.008
https://doi.org/10.1016/j.csda.2016.09.008
Publikováno v:
Environmental Pollution. 290:118039
We address the source characterization of atmospheric releases using adaptive strategies in Bayesian inference in combination with the numerical solution of the dispersion problem by a stabilized finite element method and uncertainty quantification i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28df045187a7c3dab52e9c14b309d85a
https://doi.org/10.1016/j.envpol.2021.118039
https://doi.org/10.1016/j.envpol.2021.118039
Publikováno v:
Braz. J. Probab. Stat. 33, no. 4 (2019), 756-781
Observation and parameter driven models are commonly used in the literature to analyse time series of counts. In this paper, we study the characteristics of a variety of models and point out the main differences and similarities among these procedure
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::508f444a2994a72fd63394754f52d2f2
https://doi.org/10.1214/19-bjps437
https://doi.org/10.1214/19-bjps437
A dynamic linear model with extended skew-normal for the initial distribution of the state parameter
Publikováno v:
Computational Statistics & Data Analysis. 74:64-80
We develop a Bayesian dynamic model for modeling and forecasting multivariate time series relaxing the assumption of normality for the initial distribution of the state space parameter, and replacing it by a more flexible class of distributions, whic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b2dd42636af479f10ecaf424c39e8ed
https://doi.org/10.1016/j.csda.2013.12.008
https://doi.org/10.1016/j.csda.2013.12.008
Publikováno v:
Computational Statistics. 28:2267-2293
A multimove sampling scheme for the state parameters of non-Gaussian and nonlinear dynamic models for univariate time series is proposed. This procedure follows the Bayesian framework, within a Gibbs sampling algorithm with steps of the Metropolis---
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9a33e78430b655ab70bb9fe6733ac6d5
https://doi.org/10.1007/s00180-013-0406-9
https://doi.org/10.1007/s00180-013-0406-9