جستجوی مقالات مرتبط با کلیدواژه "gini index" در نشریات گروه "پزشکی"

The purpose of this study is to investigate the relationship between natural disasters and income inequality in Indonesia, a developing country with a high risk of natural hazards and high population densities in disaster-prone regions. This paper used cross-province panel data during the period between 2010 and 2016. Natural disasters data were obtained from Indonesia National Agency for Disaster Management (BNPB), while Gini index data as an indicator of income inequality were obtained from Indonesia Central Bureau of Statistics (BPS). To estimate the impact of natural disasters on income inequality, this paper used a fixed effect regression model. It was found that the lagged variable of natural disasters positively affected the Gini index. The coefficient of this variable was 0.0093 at a significance level of 5%. It indicated that natural disasters worsen income inequality. This study also showed that natural disasters that negatively affected the Gini index were hydrological disasters in year t (between  -0.0179 and -0.0199 at a significance level of 0.1%).  Meanwhile, meteorological disasters tended to increase income inequality in the subsequent years. The coefficients were 0.0282 and 0.0187, and were statistically significant at least 5%. In addition, meteorological disasters consistently affected income distribution in all Western Indonesia (Sumatra, Java, and Bali) and other islands. The coefficients were 0.0205 and 0.0510 at a significance level of 5% indicating that meteorological disasters tended to increase inequality in income distribution.But these climatological disasters had a negative impact on income distribution in other islands in years t and t-1 (-0.0192 and -0.0680 at the significance level of 1% and 5%, respectively).  The findings of this study imply that designing policies to deal with inequality at the regional level should also concern the different influences of various natural disasters on income inequality.

A great number of data mining methods have been widely made such as gene regulatory networks and gene set analyses to connect genes that reveal similar expression patterns. These methods generally fail to unveil gene-gene interactions in the same cluster. The aim of this study is to use several nonparametric correlation coefficient methods to transform the linear rank statistics into distance metrics on a Saccharomyces cerevisiae data set.

These nonparametric correlation coefficients, Kendall’s tau index and Gini rank correlation, were compared with common Pearson correlation method. The reliability and advantages of our proposed is satisfied using genetic website, http://www.yeast genome .org/. To address the interactions and characterize the gene–gene biological processes explicitly, the gene relationships are shown as a Pajek graph topology.

The results of biological interactions and characteristics demonstrated that the proposed nonparametric correlation coefficient methods have a strong capability to identify interaction genes. Moreover, suggested techniques could accurately detect the main genes and functional interactions in comparison to generally used Pearson correlation coefficient.

The two non-linear correlation coefficient techniques are proposed to measure the gene interactions more precisely.