{"title":"均值-方差和均值-方差-相关神经网络回归模型","authors":"Andrea Gabrielli","doi":"10.2139/ssrn.3438332","DOIUrl":null,"url":null,"abstract":"We introduce two neural network models designed for application in statistical learning. The mean-variance neural network regression model allows us to simultaneously model the mean and the variance of a response variable. In case of a two-dimensional response vector, the mean-variance-correlation neural network regression model enables us to jointly model the means, the variances and the correlation.","PeriodicalId":363330,"journal":{"name":"Computation Theory eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Mean-Variance and Mean-Variance-Correlation Neural Network Regression Models\",\"authors\":\"Andrea Gabrielli\",\"doi\":\"10.2139/ssrn.3438332\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce two neural network models designed for application in statistical learning. The mean-variance neural network regression model allows us to simultaneously model the mean and the variance of a response variable. In case of a two-dimensional response vector, the mean-variance-correlation neural network regression model enables us to jointly model the means, the variances and the correlation.\",\"PeriodicalId\":363330,\"journal\":{\"name\":\"Computation Theory eJournal\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computation Theory eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3438332\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computation Theory eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3438332","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mean-Variance and Mean-Variance-Correlation Neural Network Regression Models
We introduce two neural network models designed for application in statistical learning. The mean-variance neural network regression model allows us to simultaneously model the mean and the variance of a response variable. In case of a two-dimensional response vector, the mean-variance-correlation neural network regression model enables us to jointly model the means, the variances and the correlation.