{"title":"基于带宽参数变化的gini指数的核估计","authors":"K. Agbokou, Y. Mensah","doi":"10.37418/amsj.11.12.6","DOIUrl":null,"url":null,"abstract":"Most of the measures of income inequality are derived from the Lorenz curve, and many authors state that the Gini index is the best single measure of inequality. The present paper reviews some of the theorical properties of the Lorenz curve and provides a nonparametric estimate of the Gini index and the almost sure convergence of this estimate. And to confirm the performance of the estimator, an application on real data was carried out.","PeriodicalId":231117,"journal":{"name":"Advances in Mathematics: Scientific Journal","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"KERNEL’S ESTIMATION OF GINI INDEX BASED ON THE VARYING BANDWIDTH PARAMETER\",\"authors\":\"K. Agbokou, Y. Mensah\",\"doi\":\"10.37418/amsj.11.12.6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Most of the measures of income inequality are derived from the Lorenz curve, and many authors state that the Gini index is the best single measure of inequality. The present paper reviews some of the theorical properties of the Lorenz curve and provides a nonparametric estimate of the Gini index and the almost sure convergence of this estimate. And to confirm the performance of the estimator, an application on real data was carried out.\",\"PeriodicalId\":231117,\"journal\":{\"name\":\"Advances in Mathematics: Scientific Journal\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Mathematics: Scientific Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.37418/amsj.11.12.6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Mathematics: Scientific Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37418/amsj.11.12.6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
KERNEL’S ESTIMATION OF GINI INDEX BASED ON THE VARYING BANDWIDTH PARAMETER
Most of the measures of income inequality are derived from the Lorenz curve, and many authors state that the Gini index is the best single measure of inequality. The present paper reviews some of the theorical properties of the Lorenz curve and provides a nonparametric estimate of the Gini index and the almost sure convergence of this estimate. And to confirm the performance of the estimator, an application on real data was carried out.
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