Title :
Autoregressive models with epsilon-skew-normal innovations
Author_Institution :
Univ. Paris-Sud, Gif-sur-Yvette, France
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.
Keywords :
Monte Carlo methods; autoregressive moving average processes; maximum likelihood estimation; Monte Carlo simulation; asymmetric near-Gaussian correlated signals; autoregressive models; epsilon-skew-normal innovations; maximum likelihood estimators; Covariance matrices; Data models; Mathematical model; Maximum likelihood estimation; Random variables; Technological innovation; Time series analysis; Non-Gaussian; autoregressive model; maximum likelihood estimation; skewness;
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European
Conference_Location :
Lisbon
