{"title":"基于LASSO和SCAD的缺失数据分位数模型半参数估计","authors":"Aws Adnan Al-Tai, Qutaiba N. Nayef Al-Kazaz","doi":"10.33095/jeas.v28i133.2351","DOIUrl":null,"url":null,"abstract":"In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method","PeriodicalId":53940,"journal":{"name":"Eskisehir Osmangazi Universitesi IIBF Dergisi-Eskisehir Osmangazi University Journal of Economics and Administrative Sciences","volume":"53 1","pages":""},"PeriodicalIF":0.3000,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Semi parametric Estimators for Quantile Model via LASSO and SCAD with Missing Data\",\"authors\":\"Aws Adnan Al-Tai, Qutaiba N. Nayef Al-Kazaz\",\"doi\":\"10.33095/jeas.v28i133.2351\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method\",\"PeriodicalId\":53940,\"journal\":{\"name\":\"Eskisehir Osmangazi Universitesi IIBF Dergisi-Eskisehir Osmangazi University Journal of Economics and Administrative Sciences\",\"volume\":\"53 1\",\"pages\":\"\"},\"PeriodicalIF\":0.3000,\"publicationDate\":\"2022-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Eskisehir Osmangazi Universitesi IIBF Dergisi-Eskisehir Osmangazi University Journal of Economics and Administrative Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.33095/jeas.v28i133.2351\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Eskisehir Osmangazi Universitesi IIBF Dergisi-Eskisehir Osmangazi University Journal of Economics and Administrative Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33095/jeas.v28i133.2351","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ECONOMICS","Score":null,"Total":0}
Semi parametric Estimators for Quantile Model via LASSO and SCAD with Missing Data
In this study, we made a comparison between LASSO & SCAD methods, which are two special methods for dealing with models in partial quantile regression. (Nadaraya & Watson Kernel) was used to estimate the non-parametric part ;in addition, the rule of thumb method was used to estimate the smoothing bandwidth (h). Penalty methods proved to be efficient in estimating the regression coefficients, but the SCAD method according to the mean squared error criterion (MSE) was the best after estimating the missing data using the mean imputation method
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