{"title":"具有偏态创新的自回归模型","authors":"P. Bondon","doi":"10.5281/ZENODO.44204","DOIUrl":null,"url":null,"abstract":"We consider the problem of modelling asymmetric near-Gaussian correlated signals by autoregressive models with epsilon-skew normal innovations. Moments and maximum likelihood estimators of the parameters are proposed and their limit distributions are derived. Monte Carlo simulation results are analyzed and the model is fitted to a real time series.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Autoregressive models with epsilon-skew-normal innovations\",\"authors\":\"P. Bondon\",\"doi\":\"10.5281/ZENODO.44204\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We consider the problem of modelling asymmetric near-Gaussian correlated signals by autoregressive models with epsilon-skew normal innovations. Moments and maximum likelihood estimators of the parameters are proposed and their limit distributions are derived. Monte Carlo simulation results are analyzed and the model is fitted to a real time series.\",\"PeriodicalId\":198408,\"journal\":{\"name\":\"2014 22nd European Signal Processing Conference (EUSIPCO)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-11-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 22nd European Signal Processing Conference (EUSIPCO)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5281/ZENODO.44204\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 22nd European Signal Processing Conference (EUSIPCO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5281/ZENODO.44204","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Autoregressive models with epsilon-skew-normal innovations
We consider the problem of modelling asymmetric near-Gaussian correlated signals by autoregressive models with epsilon-skew normal innovations. Moments and maximum likelihood estimators of the parameters are proposed and their limit distributions are derived. Monte Carlo simulation results are analyzed and the model is fitted to a real time series.