In the last years several probability distributions have been proposed in the literature, especially with the aim of obtaining models that are more flexible relative to the behaviors of the density and hazard rate functions. For instance, Ghitany et al. (2013) proposed a new generalization of the Lindley distribution, called power Lindley distribution, whereas Sharma et al. (2015a) proposed the inverse Lindley distribution. From these two generalizations Barco et al. (2017) studied the inverse power Lindley distribution, also called by Sharma et al. (2015b) as generalized inverse Lindley distribution. Considering the inverse power Lindley distribution, in this paper is evaluate the performance, through Monte Carlo simulations, with respect to the bias and consistency of nine different methods of estimations (the maximum likelihood method and eight others based on the distance between the empirical and theoretical cumulative distribution function). The numerical results showed a better performance of the estimation method based on the Anderson-Darling test statistic. This conclusion is also observed in the analysis of two real data sets.
Methodologies for identifying multivariate outliers are extremely important in statistical analysis. Outliers may reveal relevant information to variables under investigation. Statistical applications without prior identification of possible extreme values may yield controversial results and induce mistaken decision making. In many contexts, outliers are points of great practical interest. Given this, this paper seeks to discuss methodologies for the detection of multivariate outliers through a fair and adequate comparative technique in their simulation procedure. The comparison considers detection techniques based on Mahalanobis distance, besides a methodology based on cluster analysis technique. Sensitivity, specificity, and accuracy metrics are used to measure the method quality. An analysis of the computational time required to perform the procedures is evaluated. The technique based on cluster analysis revealed a noticeable superiority over the others in detection quality and also in execution time.
Criada em 11 de julho de 1951, a CAPES desenvolve ações voltadas à diversas linhas de atuação, com destaque para o apoio à pós-graduação brasileira, atuando como órgão financiador e regulador. Dentre estas linhas de atuação, destaca-se a avaliação periódica dos programas de pós-graduação, que é utilizada na alocação de recursos financeiros, em que programas em desenvolvimento recebam recursos suficientes para sua manutenção e evolução e programas consolidados tenham condições de buscar excelência na pesquisa científica a nível internacional. Este trabalho tem por objetivo estabelecer um modelo estatístico que permita a aferição do impacto da formação de recursos humanos e divulgação científica na avaliação periódica da CAPES na área de Ciências Agrárias I. Os resultados sugerem que, independentemente do cenário, investir no programa de pós-graduação para que a produção científica seja alavancada não só amplia as possibilidades de ascensão à conceitos CAPES superiores como também atua como fator de proteção à queda de conceito, garantindo assim um melhor posicionamento dos cursos de pós-graduação stricto sensu em âmbito nacional e internacional.
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