Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Ehlers, Ricardo"'
Autor:
Almeida, Marco Pollo, Paixao, Rafael, Ramos, Pedro, Tomazella, Vera, Louzada, Francisco, Ehlers, Ricardo
The aim of this article is to analyze data from multiple repairable systems under the presence of dependent competing risks. In order to model this dependence structure, we adopted the well-known shared frailty model. This model provides a suitable t
Externí odkaz:
http://arxiv.org/abs/2004.05217
Autor:
Danilevicz, Ian M, Ehlers, Ricardo S
Ideally, any statistical inference should be robust to local influences. Although there are simple ways to check about leverage points in independent and linear problems, more complex models require more sophisticated methods. Kullback-Leiber and Bre
Externí odkaz:
http://arxiv.org/abs/1904.03717
Autor:
Ehlers, Ricardo S
We propose a flexible model for count time series which has potential uses for both underdispersed and overdispersed data. The model is based on the Conway-Maxwell-Poisson (COM-Poisson) distribution with parameters varying along time to take serial c
Externí odkaz:
http://arxiv.org/abs/1901.07473
Autor:
Dias, David S., Ehlers, Ricardo S.
This paper presents a study using the Bayesian approach in stochastic volatility models for modeling financial time series, using Hamiltonian Monte Carlo methods (HMC). We propose the use of other distributions for the errors in the observation equat
Externí odkaz:
http://arxiv.org/abs/1712.02326
Autor:
Paixão, Rafael S., Ehlers, Ricardo S.
In this paper, we develop Bayesian Hamiltonian Monte Carlo methods for inference in asymmetric GARCH models under different distributions for the error term. We implemented Zero-variance and Hamiltonian Monte Carlo schemes for parameter estimation to
Externí odkaz:
http://arxiv.org/abs/1710.07693
Autor:
Danilevicz, Ian M, Ehlers, Ricardo S
BDSAR is an R package which estimates distances between probability distributions and facilitates a dynamic and powerful analysis of diagnostics for Bayesian models from the class of Simultaneous Autoregressive (SAR) spatial models. The package offer
Externí odkaz:
http://arxiv.org/abs/1704.07414
Transformed Generalized Autoregressive Moving Average (TGARMA) models were recently proposed to deal with non-additivity, non-normality and heteroscedasticity in real time series data. In this paper, a Bayesian approach is proposed for TGARMA models,
Externí odkaz:
http://arxiv.org/abs/1612.09561
Autor:
Ferreira, Paulo, Gonzales, Jhon, Tomazella, Vera, Ehlers, Ricardo, Louzada, Francisco, Silva, Eveliny
In this paper we propose to make Bayesian inferences for the parameters of the Lomax distribution using non-informative priors, namely the Jeffreys prior and the reference prior. We assess Bayesian estimation through a Monte Carlo study with 500 simu
Externí odkaz:
http://arxiv.org/abs/1602.08450
Autor:
Ehlers, Ricardo S
In this paper, we propose to obtain the skewed version of a unimodal symmetric density using a skewing mechanism that is not based on a cumulative distribution function. Then we disturb the unimodality of the resulting skewed density. In order to int
Externí odkaz:
http://arxiv.org/abs/1512.03341
Publikováno v:
Communications in Statistics: Case Studies, Data Analysis and Applications, 1 (2016) 192-205
Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work present
Externí odkaz:
http://arxiv.org/abs/1509.08666