Jorge Alberto Achcar

Constant hazard against a change-point alternative: a bayesian approach with censored data

A Bayesian approach to the problem of a constant hazard with a single change-point is developed using noninformative reference priors. We also present a generalization for the comparison for two treatments.

Financial constraints and investment decisions: evidence from a highly unstable emerging economy

This study analyses the role of financial constraints on the investment decisions of 497 Brazilian firms. We use panel data, with firm-specific information for different years, allowing for the abandonment of the representative firm model. Information on capital intensity at the firm level is used to group firms. We estimate different models and the results suggest the presence of financial restrictions, especially for capital-intensive firms.

Bayesian Estimation of Diagnostic Tests Accuracy for Semi-Latent Data with Covariates

ABSTRACT The performance of a diagnostic test is usually summarized by its sensitivity and specificity. Sensitivity is the probability of a positive result, once the individual is truly ill, and specificity is the probability of a negative result, regarding a healthy individual. These measures are obtained by comparing the test outcome and the results of a reference test generically denominated gold standard. However, in many applied problems considering two diagnostic tests, the gold standard is not available for those individuals with negative results on both tests. In addition, not all diagnostic tests have the same performance across different populations. In this context, we present a Bayesian inference approach for performance measures estimation and we propose an extension of this procedure involving the inclusion of covariates. This Bayesian approach is based on Markov Chain Monte Carlo methods. The conditional dependence between the diagnostic tests was considered. As an example, we applied the proposed methodology to a real data set obtained from the medical literature.

Uma abordagem bayesiana para decisões de investimentos

This study analyses the investment decisions of 497 Brazilian firms in the period 1986-97. The role of financial constraints are considered both theoretically and empirically, through the use of Bayesian econometric models. We use longitudinal data, with firm-specific information for different years, allowing for the abandonment of the representative firm model. Information on capital intensity at the firm level allows for the separation of firms according to this variable, and makes it possible to consider information asymmetries. We estimate two different models, and the results suggest the presence of financial constraints, especially for capital-intensive firms.

Use of bayesian analysis to design of clinical trials with one treatment

Assuming a Weibull distribution, the posterior distribution for the median survival time is derived in the presence of arbitrary right censorship. In the design of clinical triaLs, suppose k follow-up periods have been completed and it is desired to plan the follow-up period k+1. In this context, criteria are presented that can be employed in determining the number of new patients to be enrolled in the follow-up period k+1.

BAYESIAN ANALYSIS FOR THE SUPERPOSITION OF TWO DEPENDENT NONHOMOGENEOUS POISSON PROCESSES

ABSTRACT A Bayesian analysis for the superposition of two dependent nonhomogenous Poisson processes is studied by means of a bivariate Poisson distribution. This particular distribution presents a new likelihood function which takes into account the correlation between the two nonhomogenous Poisson processes. A numerical example using Markov Chain Monte Carlo method with data augmentation is considered.

The log-linear model with a generalized gamma distribution for the error: A Bayesian approach

Considering a log-linear model with one covariate and a generalized gamma distribution for the error, we find the posterior densities for the parameters of interest. Since many standard survival distributions are particular cases of the generalized gamma model, the proposed bayesian method is very useful to discriminate between possible models to be used in the data analysis. The Laplace approximation for integrals (see Tierney and Kadane, 1984) is used to find the posterior distributions of the parameters involved when they cannot be obtained explicitly.

SOME PRACTICAL CONSIDERATIONS ABOUT PRECISION OF ESTIMATORS OF MINIMUM SQUARES AND LIKELIHOOD FOR THE PARAMETERS OF LINEAR REGRESSION NORMAL

In this paper, we discuss some practical considerations on the use of different methods for the estimation of linear regression parameters and their impact on the obtained inferences. In practice, it is common to use some existing statistical software to obtain the least squares estimators (MSE) for the regression parameters and consider a non-biased estimator for the variance based on the residual squared sum of the residuals. Alternatively, we discuss the use of the maximum likelihood estimator (MLE) for the variance of the error (an unbiased estimator, but with smaller squared error) which may lead to different conclusions in terms of inferences. Two numerical examples are presented to illustrate the proposed method.

A seasonal and heteroscedastic gamma model for hydrological time series: A Bayesian approach

Time series models are often used in hydrology to model streamflow series in order to forecast and generate synthetic series which are inputs for the analysis of complex water resources systems. In this paper, we introduce a new modeling approach for hydrologic time series assuming a gamma distribution for the data, where both the mean and conditional variance are being modeled. Bayesian methods using standard Markov Chain Monte Carlo Methods (MCMC) and a simulation algorithm introduced by [1] are used to simulate samples of the joint posterior distribution of interest. An example is given with a time series of monthly averages of natural streamflows, measured from 1931 to 2010 in Furnas hydroelectric dam, in southeastern Brazil.