Ricardo S Ehlers

• PhD
• University of São Paulo

In this work, we present classical and Bayesian inferential methods based on samples in the presence of progressive type-II censoring under the Very Flexible Weibull (VFW) distribution. The considered distribution is relevant because it is an alternative to traditional non-flexible distributions and also to some flexible distributions already known...

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 Bregman divergences were already applied in Bayesian inference to measure the isolated impact of each o...

The aim of this article is to analyze multiple repairable systems data under the presence of dependent competing risks. It is known that the dependence effect in this scenario influences the estimates of the model parameters. Hence, under the assumption that the cause-specific intensities follow a power law process (PLP), we propose a frailty-induc...

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 theoretical basis for generating dependence between the components failure times in the dependent com...

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 simulated data sets. To evaluate the possible impact of prior specification on estimation, two criteria...

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 Bregman divergences were already applied in Bayesian inference to measure the isolated impact of each o...

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 correlation into account. Model estimation is challenging however and require the application of rece...

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 equation of stochastic volatility models, besides the Gaussian distribution, to address problems as heavy...

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 try and reduce the standard errors of the estimates thus obtaing more efficient results at the pric...

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 offers a new and fine plot to compare models as well as it works in an intuitive way to allow any analyst...

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, thus extending the original model. We conducted a simulation study to investigate the performance o...

In this paper we perform Bayesian estimation of stochastic volatility models with heavy tail distributions using Metropolis adjusted Langevin (MALA) and Riemman manifold Langevin (MMALA) methods. We provide analytical expressions for the application of these methods, assess the performance of these methodologies in simulated data and illustrate the...

In a cloud computing environment, companies have the ability to allocate resources according to demand. However, there is a delay that may take minutes between the request for a new resource and it being ready for using. This causes the reactive techniques, which request a new resource only when the system reaches a certain load threshold, to be no...

This work has as objective to develop, compare and apply stochastic simulation techniques for DCC-GARCH Models with multivariate distributions using the Bayesian approach. Both parameter estimation and model comparison are not trivial tasks and the Hamiltonian Monte Carlo method will be applied to this end, for this use for the package ”rstan”in R...

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 approach using both traditional Metropolis-Hastings methods and Hamiltonian Monte Carlo (HMC) methods to obtain the approximations to the posterior marginal distributions...

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 simulated data sets. To evaluate the possible impact of prior specification on estimation, two criteria...

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 introduce skewness we use the general method which transforms any continuous unimodal and symmetric dis...

In a cloud computing environment, companies can allocate and de-allocate computing resources according to demand. However, this task does not happen instantaneously. There is a delay, which may take minutes, between the request for a new resource and it be ready for use. To resolve this problem we need forecast the future demand for then allocate t...

In this paper we perform Bayesian estimation of stochastic volatility models with heavy tail distributions using Metropolis adjusted Langevin (MALA) and Riemman manifold Langevin (MMALA) methods. We provide analytical expressions for the application of these methods, assess the performance of these methodologies in simulated data and illustrate the...

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 of these components must be equal to one. Compositional data is present in different knowledge areas, as in geology, economy, medicine among many others. In this paper,...

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 presents Bayesian approach for GARMA models with Poisson, binomial and negative binomial distributions. A s...

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-parameter Kumaraswamy Inverse Weibull distribution with decreasing and unimodal failure rate is introduced. We provide a comprehensive treatment of the mathematical prope...

Statistical modeling in political analysis is used recently to describe electoral behaviour of political party. In this chapter we propose aWeibull mixture model that describes the votes obtained by a political party in Brazilian presidential elections. We considered the votes obtained by the Workers’ Party in five presidential elections from 1994...

In this article, we study and compare different proposals of heavy-tailed (possibly skewed) distributions as robust alternatives to the normal model. The density functions are all represented as scale mixtures, which enables efficient Bayesian estimation via Markov chain Monte Carlo (MCMC) methods. However, although the symmetric versions of these...

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 practice due to the lack of reliable software. This paper describes the {\tt R} package {\bf BayesDccGarch} which was developed to implement recently proposed inference proc...

In this paper we assess Bayesian estimation and prediction using integrated Laplace approximation (INLA) on a stochastic volatility model. This was performed through a Monte Carlo study with 1000 simulated time series. To evaluate the estimation method, two criteria were considered: the bias and square root of the mean square error (smse). The crit...

The main goal in this paper is to develop and apply stochastic simulation techniques for GARCH models with multivariate skewed distributions using the Bayesian approach. Both parameter estimation and model comparison are not trivial tasks and several approximate and computationally intensive methods (Markov chain Monte Carlo) will be used to this e...

In this paper we use Markov chain Monte Carlo (MCMC) methods in order to estimate and compare GARCH models from a Bayesian perspective. We allow for possibly heavy tailed and asymmetric distributions in the error term. We use a general method proposed in the literature to introduce skewness into a continuous unimodal and symmetric distribution. For...

In this paper, we use Markov Chain Monte Carlo (MCMC) methods in order to estimate and compare stochastic production frontier models from a Bayesian perspective. We consider a number of competing models in terms of different production functions and the distribution of the asymmetric error term. All MCMC simulations are done using the package JAGS...

In this paper, we show how the construction of a trans-dimensional equivalent of the Gibbs sampler can be used to obtain a powerful suite of adaptive algorithms suitable for trans-dimensional MCMC samplers. These algorithms adapt at the local scale, optimizing performance at each iteration in contrast to the globally adaptive scheme proposed by oth...

In this paper we extend the work of Brooks and Ehlers (2002) and Brooks et al. (2003) by constructing efficient proposal schemes for reversible jump MCMC in the context of autoregressive moving average models. In particular, the full conditional distribution is not available for the added parameters and ap-proximations to it are provided by suggest...

Spatial models have been used in many fields of science where the data are collected in different locations, i.e. each observation is associated to a point in space. In particular, the analysis of spatial dispersion of the risk of occurrence of a certain event is in general performed via maps of incidence, where a set of areas is shaded according t...

We develop for regression models trans-dimensional genetic algorithms for the exploration of large model spaces. Our algorithms can be used in two dieren t ways. The rst possibility is to search the best model according to some criteria such as AIC or BIC. The second possibility is to use our algorithms to explore the model space, search for the mo...

Spatial models have been used in many flelds of science where the data are collected in difierent locations, i.e. each observation is associated to a point in space. In particular, the analysis of spatial dispersion of the risk of occurrence of a certain event is in general performed via maps of incidence, where a set of areas is shaded according t...

Forecasting the levels of vector autoregressive (VAR) log-transformed time series has shown to be awkward by Ari~no and Franses (1996) who realised that just exponentiating the forecasts was a naive procedure due to the ocurrence of bias. They pr oposed a new manner to forecast untransformed VAR through correcting the log-transformed forecasts, and...

Practical use of Bayesian methods usually involves obtaining certain characteristics of the posterior distribution of the parameter of interest. However, if the related integrals do not have exact analytical solutions it is necessary to use numerical approximation, Monte Carlo integration or analytic approximation. In this paper, the use of the Lap...

Brooks et al. (2003) introduce a framework for constructing efficient proposal schemes for reversible jump MCMC algorithms, but consider only jumps between nested models differing in dimension by only one and for which the reverse move is deterministic. They illustrate their ideas within the context of several examples including the study of an aut...

Resumo. Modelagem de volatilidade desempenha um papel fundamental em Econometria. Neste trabalho são estudados a generalização dos modelos autorregressivos condicionalmente heterocedásticos conhecidos como GARCH e sua principal generalização multivariada, os modelos DCC-GARCH (Dynamic Condicional Correlation GARCH). Para os erros desses modelos são...

The aim of this work was to study models for bivariate time series, where the dependence structure among the series is modeled by copulas. The advantage of this approach is that copulas provide a complete description of dependence structure. In terms of inference was adopted the Bayesian approach with utilization of Markov chain Monte Carlo (MCMC)...