• DocumentCode
    2054773
  • Title

    AR processes with non-Gaussian asymmetric innovations

  • Author

    Bondon, Pascal ; Li Song

  • Author_Institution
    Univ. Paris-Sud, Gif-sur-Yvette, France
  • fYear
    2013
  • fDate
    9-13 Sept. 2013
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    We consider the problem of modeling non-Gaussian correlated signals by autoregressive models with skew exponential power innovations. Generalized moments and maximum likelihood estimators of the parameters are proposed and large sample properties are established. Finite sample behavior of the estimators is studied via Monte Carlo simulations. An application to real data is considered.
  • Keywords
    Monte Carlo methods; autoregressive processes; maximum likelihood estimation; AR processes; Monte Carlo simulations; generalized moments; maximum likelihood estimators; non-Gaussian asymmetric innovations; non-Gaussian correlated signals; skew exponential power innovations; Biological system modeling; Covariance matrices; Data models; Maximum likelihood estimation; Noise; Technological innovation; Non-Gaussian; asymmetric distribution; autoregressive model; maximum likelihood estimation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European
  • Conference_Location
    Marrakech
  • Type

    conf

  • Filename
    6811489
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2054773