The Poisson-exponential lifetime distribution

In this paper we proposed a new two-parameters lifetime distribution with increasing failure rate. The new distribution arises on a latent complementary risk problem base. The properties of the proposed distribution are discussed, including a formal proof of its probability density function and explicit algebraic formulae for its reliability and failure rate functions, quantiles and moments, including the mean and variance. A simple EM-type algorithm for iteratively computing maximum likelihood estimates is presented. The Fisher information matrix is derived analytically in order to obtaining the asymptotic covariance matrix. The methodology is illustrated on a real data set. © 2010 Elsevier B.V. All rights reserved.

The complementary exponential power lifetime model

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The Poisson-exponential distribution: a Bayesian approach

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A bayesian analysis for the parameters of the exponential-logarithmic distribution

The exponential-logarithmic is a new lifetime distribution with decreasing failure rate and interesting applications in the biological and engineering sciences. Thus, a Bayesian analysis of the parameters would be desirable. Bayesian estimation requires the selection of prior distributions for all parameters of the model. In this case, researchers usually seek to choose a prior that has little information on the parameters, allowing the data to be very informative relative to the prior information. Assuming some noninformative prior distributions, we present a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Jeffreys prior is derived for the parameters of exponential-logarithmic distribution and compared with other common priors such as beta, gamma, and uniform distributions. In this article, we show through a simulation study that the maximum likelihood estimate may not exist except under restrictive conditions. In addition, the posterior density is sometimes bimodal when an improper prior density is used. © 2013 Copyright Taylor and Francis Group, LLC.

Análise Bayesiana para a distribuição Exponencial-Logarítmica

Pós-graduação em Matematica Aplicada e Computacional - FCT

Strategic capacitor placement in distribution systems by minimisation of harmonics amplification because of resonance

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Bayesian estimation of generalized exponential distribution under noninformative priors

The generalized exponential distribution, proposed by Gupta and Kundu (1999), is a good alternative to standard lifetime distributions as exponential, Weibull or gamma. Several authors have considered the problem of Bayesian estimation of the parameters of generalized exponential distribution, assuming independent gamma priors and other informative priors. In this paper, we consider a Bayesian analysis of the generalized exponential distribution by assuming the conventional non-informative prior distributions, as Jeffreys and reference prior, to estimate the parameters. These priors are compared with independent gamma priors for both parameters. The comparison is carried out by examining the frequentist coverage probabilities of Bayesian credible intervals. We shown that maximal data information prior implies in an improper posterior distribution for the parameters of a generalized exponential distribution. It is also shown that the choice of a parameter of interest is very important for the reference prior. The different choices lead to different reference priors in this case. Numerical inference is illustrated for the parameters by considering data set of different sizes and using MCMC (Markov Chain Monte Carlo) methods.

Distribution of refractory and volatile elements in CoRoT exoplanet host stars

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Study on the Curcumin dynamics and distribution through living biofilms

Human oral cavity is colonized by a wide range of microorganisms, often organized in biofilms. These biofilms are responsible for the pathogenesis of caries and most periodontal diseases. A possible alternative to reduce biofilms is the photodynamic inactivation (PDI). The success of the PDI depends on different factors. The time required by the PS to remain in contact with the target cells prior to illumination is determinant for the technique's efficacy. This study aimed to assess the interaction between the PS and the biofilm prior to the PDI. We used confocal microscopy and FLIM to evaluate the interaction between the PS and the biofilm's microorganism during the pre-irradiation time (PIT). The study of this dynamics can lead to the understanding of why only some PSs are effective and why is necessary a long PIT for some microorganisms. Our results showed that are differences for each PIT. These differences can be the determinate for the efficacy of the PDI. We observed that the microorganism needs time to concentrate and/or transport the PS within the biofilm. We presented preliminary results for biofilms of Candida albicans and Streptococcus mutans in the presence of Curcumin and compared it with the literature. We observed that the effectiveness of the PDI might be directly correlated to the position of the PS with the biofilm. Further analyses will be conducted in order to confirm the potential of FLIM to assess the PS dynamics within the biofilms. © 2013 SPIE.

Lifetime comparison of Y-TZP/porcelain crowns under different loading conditions

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

The FGM bivariate lifetime copula model: a bayesian approach

In this paper, we propose a bivariate distribution for the bivariate survival times based on Farlie-Gumbel-Morgenstern copula to model the dependence on a bivariate survival data. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo (MCMC) is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated via a simulation study and a real dataset.