{"title":"t -方法与稀疏建模的应用","authors":"Ryo Asano, Masato Ohkubo, Shinto Eguchi, Yasushi Nagata","doi":"10.17929/tqs.9.1","DOIUrl":null,"url":null,"abstract":"The Mahalanobis-Taguchi(MT) system is widely used and is one of the quality engineering methods(Taguchi method). Taguchi’s T-method is suitable for regression issues among MTsystems. In addition, there is an improved version of the T-method called the Ta-method.This study examinedthe effect of variable selection using the Ta method. Specifically, we consider two lasso’s single regression forvariable selection before applying the Ta method and evaluate their performanceusing Monte Carlo simulations.","PeriodicalId":486869,"journal":{"name":"Total quality science","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The T-method with the application of sparse modeling\",\"authors\":\"Ryo Asano, Masato Ohkubo, Shinto Eguchi, Yasushi Nagata\",\"doi\":\"10.17929/tqs.9.1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Mahalanobis-Taguchi(MT) system is widely used and is one of the quality engineering methods(Taguchi method). Taguchi’s T-method is suitable for regression issues among MTsystems. In addition, there is an improved version of the T-method called the Ta-method.This study examinedthe effect of variable selection using the Ta method. Specifically, we consider two lasso’s single regression forvariable selection before applying the Ta method and evaluate their performanceusing Monte Carlo simulations.\",\"PeriodicalId\":486869,\"journal\":{\"name\":\"Total quality science\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Total quality science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17929/tqs.9.1\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Total quality science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17929/tqs.9.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The T-method with the application of sparse modeling
The Mahalanobis-Taguchi(MT) system is widely used and is one of the quality engineering methods(Taguchi method). Taguchi’s T-method is suitable for regression issues among MTsystems. In addition, there is an improved version of the T-method called the Ta-method.This study examinedthe effect of variable selection using the Ta method. Specifically, we consider two lasso’s single regression forvariable selection before applying the Ta method and evaluate their performanceusing Monte Carlo simulations.