Leandro Chaves Rêgo

Corrigendum to “Estimation of expected number of accidents and workforce unavailability through Bayesian population variability analysis and Markov-based model” [Reliab. Eng. Syst. Saf. 150 (2016) 136–146]

a CEERMA-Center for Risk Analysis, Reliability and Environmental Modeling, Universidade Federalde Pernambuco, Av. da Arquitetura, s/n, CEP: 50.740-550, Cidade Universitária, Recife, PE, Brazil b Department of Production Engineering, Universidade Federalde Pernambuco, Rua Acadêmico Hélio Ramos, s/n, Cidade Universitária, CEP: 50740-530, Recife, PE, Brazil c Mechanical Engineering Department, University of Chile, Santiago, Chile d Department of Statistics and Applied Mathematics, Federal University of Ceará, Avenidada Universidade, 2853-Benfica, Fortaleza 60020-181, CE, Brazil e CHESF-SãoFrancisco’s Hydroelectric Company, Rua Delmiro Gouvêia, 333-San Martin, CEP:50761-901, Recife, PE, Brazil f Academic Unit of Garanhuns, Federal Rural University of Pernambuco, Garanhuns, Brazil

The gamma generalized normal distribution: A descriptor of SAR imagery

Abstract We propose a new four-parameter distribution for modeling synthetic aperture radar (SAR) imagery named the gamma generalized normal (GGN) by combining the gamma and generalized normal distributions. A mathematical characterization of the new distribution is provided by identifying the limiting behavior and by calculating the density and moment expansions. The GGN model performance is evaluated on both synthetic and actual data and, for that, maximum likelihood estimation and random number generation are discussed. The proposed distribution is compared with the beta generalized normal (BGN) distribution, which has already shown to appropriately represent SAR imagery. The performance of these two distributions are measured by means of statistics which provide evidence that the GGN can outperform the BGN distribution in some contexts.

Random-Subset Voting

Most of the voting procedures in the literature assume that voters have complete and transitive preferences over the alternatives. A basic premise is that any voter is able to evaluate any pair of alternatives in a set and select his/her preferred one, or indicate indifference between them. Nevertheless, some researchers have highlighted that voters, as humans, have limited capacity to deal with and consequently compare big sets of alternatives. In this paper, we propose the Random-Subset Voting, a voting procedure that through a random approach allows the voters to evaluate less alternatives. Instead of analyzing the entire set of alternatives, each voter will evaluate a random subset of a pre-determined size. We have proposed a theorem indicating that, for large sets of voters, the outcomes of traditional Borda and Random-Subset Borda converge. We have also implemented a web experiment and a Monte Carlo simulation in order to validate the proposed procedure and analyze how it behaves in several scenarios.