{"title":"分位数和分位数密度函数的非参数估计","authors":"X. Yang, H. Ad, D. Wang","doi":"10.4172/2155-6180.1000356","DOIUrl":null,"url":null,"abstract":"In this article, we derive a new and unique method of estimating quantile and quantile density function, which is based on moments of fractional order statistics. A comparison of the proposed estimators is made with existing popular nonparametric quantile and quantile density estimators, in terms of mean squared error (MSE) for censored and uncensored data. Recommendations for the choice of quantile and/or quantile density estimators are given.","PeriodicalId":87294,"journal":{"name":"Journal of biometrics & biostatistics","volume":"8 1","pages":"1-11"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Nonparametric Estimation of Quantile and Quantile Density Function\",\"authors\":\"X. Yang, H. Ad, D. Wang\",\"doi\":\"10.4172/2155-6180.1000356\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article, we derive a new and unique method of estimating quantile and quantile density function, which is based on moments of fractional order statistics. A comparison of the proposed estimators is made with existing popular nonparametric quantile and quantile density estimators, in terms of mean squared error (MSE) for censored and uncensored data. Recommendations for the choice of quantile and/or quantile density estimators are given.\",\"PeriodicalId\":87294,\"journal\":{\"name\":\"Journal of biometrics & biostatistics\",\"volume\":\"8 1\",\"pages\":\"1-11\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of biometrics & biostatistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4172/2155-6180.1000356\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of biometrics & biostatistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4172/2155-6180.1000356","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nonparametric Estimation of Quantile and Quantile Density Function
In this article, we derive a new and unique method of estimating quantile and quantile density function, which is based on moments of fractional order statistics. A comparison of the proposed estimators is made with existing popular nonparametric quantile and quantile density estimators, in terms of mean squared error (MSE) for censored and uncensored data. Recommendations for the choice of quantile and/or quantile density estimators are given.
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