{"title":"长期收益分配模型与非参数市场风险估计","authors":"S. Dutta, Tushar Kanti Powdel","doi":"10.1007/s13571-023-00303-x","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":45608,"journal":{"name":"Sankhya-Series B-Applied and Interdisciplinary Statistics","volume":"85 1","pages":"257-289"},"PeriodicalIF":0.6000,"publicationDate":"2023-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling Long Term Return Distribution and Nonparametric Market Risk Estimation\",\"authors\":\"S. Dutta, Tushar Kanti Powdel\",\"doi\":\"10.1007/s13571-023-00303-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":45608,\"journal\":{\"name\":\"Sankhya-Series B-Applied and Interdisciplinary Statistics\",\"volume\":\"85 1\",\"pages\":\"257-289\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-02-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sankhya-Series B-Applied and Interdisciplinary Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s13571-023-00303-x\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sankhya-Series B-Applied and Interdisciplinary Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s13571-023-00303-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
期刊介绍:
Sankhya, Series A, publishes original, high quality research articles in various areas of modern statistics, such as probability, theoretical statistics, mathematical statistics and machine learning. The areas are interpreted in a broad sense. Articles are judged on the basis of their novelty and technical correctness.
Sankhya, Series B, primarily covers applied and interdisciplinary statistics including data sciences. Applied articles should preferably include analysis of original data of broad interest, novel applications of methodology and development of methods and techniques of immediate practical use. Authoritative reviews and comprehensive discussion articles in areas of vigorous current research are also welcome.