具有偏态创新的自回归模型

2014 22nd European Signal Processing Conference (EUSIPCO) Pub Date : 2014-11-13 DOI:10.5281/ZENODO.44204

P. Bondon

{"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}

引用次数: 0

摘要

研究了用带有偏态创新的自回归模型对非对称近高斯相关信号进行建模的问题。给出了参数的矩估计和极大似然估计，并推导了它们的极限分布。对蒙特卡罗仿真结果进行了分析，并对模型进行了拟合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2014 22nd European Signal Processing Conference (EUSIPCO)

自引率

0.00%

发文量