{"title":"实验设计中的一些组合结构:综述、统计模型及其应用","authors":"P. Valcheva, T. Oliveira","doi":"10.15406/BBIJ.2018.07.00228","DOIUrl":null,"url":null,"abstract":"Design of experiments (DOE) is an important branch of applied statistics that deals with planning, conducting of the experiment, analyzing and interpreting final results. It combines mathematical and statistical tools, which aim at constructing optimal designs to be tested. Due to the widely application during recent decades, this science is strongly spread in many areas such as optimization, process quality control as well as product performance prediction.","PeriodicalId":90455,"journal":{"name":"Biometrics & biostatistics international journal","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Some combinatorial structures in experimental design: overview, statistical models and applications\",\"authors\":\"P. Valcheva, T. Oliveira\",\"doi\":\"10.15406/BBIJ.2018.07.00228\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Design of experiments (DOE) is an important branch of applied statistics that deals with planning, conducting of the experiment, analyzing and interpreting final results. It combines mathematical and statistical tools, which aim at constructing optimal designs to be tested. Due to the widely application during recent decades, this science is strongly spread in many areas such as optimization, process quality control as well as product performance prediction.\",\"PeriodicalId\":90455,\"journal\":{\"name\":\"Biometrics & biostatistics international journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biometrics & biostatistics international journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15406/BBIJ.2018.07.00228\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biometrics & biostatistics international journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15406/BBIJ.2018.07.00228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Some combinatorial structures in experimental design: overview, statistical models and applications
Design of experiments (DOE) is an important branch of applied statistics that deals with planning, conducting of the experiment, analyzing and interpreting final results. It combines mathematical and statistical tools, which aim at constructing optimal designs to be tested. Due to the widely application during recent decades, this science is strongly spread in many areas such as optimization, process quality control as well as product performance prediction.