18 citations · 36 across the 14 of their papers we have counts for
18 papers
Bayesian Reconciliation of Overlapping Multi-Step Forecasts: A Post-Processing Framework for Cross-Origin Coherence
Oluwatosin Bamidele Fajana, Ricardo S. Ehlers, Marinho G. Andrade
Multi-step forecasting systems, including modern artificial intelligence (AI) models, often issue overlapping predictions for the same future time from different forecast origins.…
Stochastic Volatility in Mean Models with Heavy Tails: A Fast Approximate Bayesian Inference Using Hidden Markov Models
Bruno E. Holtz, Carlos A. Abanto-Valle, Ricardo S. Ehlers +1
This paper extends the approximate Bayesian estimation framework for Stochastic Volatility in Mean (SVM) models to accommodate heavy-tailed distributions from the Scale Mixture of…
Dynamic Skewness in Stochastic Volatility Models: A Penalized Prior Approach
Bruno E. Holtz, Ricardo S. Ehlers, Adriano K. Suzuki +1
Financial time series often exhibit skewness and heavy tails, making it essential to use models that incorporate these characteristics to ensure greater reliability in the results.…
Multiple repairable systems under dependent competing risks with nonparametric Frailty
Marco Pollo Almeida, Rafael Paixao, Pedro Ramos +3
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…
Bayesian influence diagnostics using normalizing functional Bregman divergence
Ian M Danilevicz, Ricardo S Ehlers
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 comp…
A Conway-Maxwell-Poisson GARMA Model for Count Data
Ricardo S Ehlers
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-Poi…
Stochastic Volatily Models using Hamiltonian Monte Carlo Methods and Stan
David S. Dias, Ricardo S. Ehlers
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…
Zero Variance and Hamiltonian Monte Carlo Methods in GARCH Models
Rafael S. Paixão, Ricardo S. Ehlers
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-va…
BDSAR: a new package on Bregman divergence for Bayesian simultaneous autoregressive models
Ian M Danilevicz, Ricardo S Ehlers
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…
Bayesian Transformed GARMA Models
Breno S. Andrade, Marinho G. Andrade, Ricardo S. Ehlers
Transformed Generalized Autoregressive Moving Average (TGARMA) models were recently proposed to deal with non-additivity, non-normality and heteroscedasticity in real time series d…
Objective Bayesian Analysis for the Lomax Distribution
Paulo Ferreira, Jhon Gonzales, Vera Tomazella +3
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. W…
A New Class of Skewed Bimodal Distributions
Ricardo S Ehlers
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 di…
Bayesian GARMA Models for Count Data
Marinho G. Andrade, Ricardo S. Ehlers, Breno S. Andrade
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 observa…
Riemann Manifold Langevin Methods on Stochastic Volatility Estimation
Mauricio Zevallos, Loretta Gasco, Ricardo Ehlers
In this paper we perform Bayesian estimation of stochastic volatility models with heavy tail distributions using Metropolis adjusted Langevin (MALA) and Riemman manifold Langevin (…
Modeling Compositional Regression with uncorrelated and correlated errors: a Bayesian approach
Taciana K. O. Shimizu, Francisco Louzada, Adriano K. Suzuki +1
Compositional data consist of known compositions vectors whose components are positive and defined in the interval (0,1) representing proportions or fractions of a "whole". The sum…
Bayesian Estimation of the Kumaraswamy Inverse Weibull Distribution
Felipe R. S. de Gusmão, Vera L. D. Tomazella, Ricardo S. Ehlers
The Kumaraswamy Inverse Weibull distribution has the ability to model failure rates that have unimodal shapes and are quite common in reliability and biological studies. The three-…
BayesDccGarch - An Implementation of Multivariate GARCH DCC Models
Jose A. Fioruci, Ricardo S. Ehlers, Francisco Louzada
Multivariate GARCH models are important tools to describe the dynamics of multivariate times series of financial returns. Nevertheless, these models have been much less used in pra…
Bayesian Inference for Generalized Extreme Value Distributions via Hamiltonian Monte Carlo
Marcelo Hartmann, Ricardo Ehlers
In this paper we propose to evaluate and compare Markov chain Monte Carlo (MCMC) methods to estimate the parameters in a generalized extreme value model. We employed the Bayesian a…