activity
20152020
most citedStochastic Volatily Models using Hamiltonian Monte Carlo Methods and Stan

3 citations · 4 across the 6 of their papers we have counts for

collaborators

9 papers

stat.AP20201 cited

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…

stat.AP2019

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…

stat.CO2019

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…

stat.AP20173 cited

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…

stat.CO2017

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…

stat.ME2016

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…