F
Fabio N. Demarqui
Researcher at Universidade Federal de Minas Gerais
Publications - 20
Citations - 227
Fabio N. Demarqui is an academic researcher from Universidade Federal de Minas Gerais. The author has contributed to research in topics: Gibbs sampling & Bayesian inference. The author has an hindex of 6, co-authored 18 publications receiving 192 citations. Previous affiliations of Fabio N. Demarqui include Federal Fluminense University & University of Connecticut.
Papers
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The diagnostic accuracy of serologic and molecular methods for detecting visceral leishmaniasis in HIV infected patients: meta-analysis.
TL;DR: Based mainly on evidence gained by infection with Leishmania infantum chagasi, serological tests should not be used to rule out a diagnosis of VL among the HIV-infected, but a positive test at even low titers has diagnostic value when combined with the clinical case definition.
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Estimating the grid of time-points for the piecewise exponential model.
TL;DR: This work extends previous works by introducing a full Bayesian approach for the piecewise exponential model in which the grid of time-points (and, consequently, the endpoints and the number of intervals) is random.
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Fully semiparametric Bayesian approach for modeling survival data with cure fraction.
TL;DR: This paper investigates the impact of assuming a random time grid for the PEM on the estimation of the cure fraction and develops an efficient collapsed Gibbs sampler algorithm for carrying out posterior computation.
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An approach to model clustered survival data with dependent censoring.
Silvana Schneider,Silvana Schneider,Fabio N. Demarqui,Enrico A. Colosimo,Vinícius Diniz Mayrink +4 more
TL;DR: This study introduces a likelihood-based method, via the Weibull and piecewise exponential distributions, capable of accommodating the dependence between failure and censoring times and devise a Monte Carlo EM algorithm to carry out inferences.
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A class of dynamic piecewise exponential models with random time grid
TL;DR: In this article, a piecewise exponential model (PEM) with random time grid is proposed for modeling survival data with explanatory variables, where a class of correlated Gamma prior distributions for the failure rates are obtained via the dynamic generalized modeling approach.