• DocumentCode
    2345677
  • Title

    Minimum complexity regression estimation with weakly dependent observations

  • Author

    Modha, Dharmendra S. ; Masry, Elias

  • Author_Institution
    Dept. of Electr. & Comput. Eng., California Univ., San Diego, La Jolla, CA, USA
  • fYear
    1994
  • fDate
    27-29 Oct 1994
  • Firstpage
    69
  • Abstract
    Given N strongly mixing observations {Xi,Yi} i=1N, we estimate the regression function f*(x)=E[Y1|X1=x], x∈ℜd from a class of neural networks, using certain minimum complexity regression estimation schemes. We establish a rate of convergence for the integrated mean squared error between the proposed regression estimator and f*
  • Keywords
    convergence of numerical methods; estimation theory; neural nets; statistical analysis; convergence rate; integrated mean squared error; minimum complexity regression estimation; neural networks; regression function; strongly mixing observations; weakly dependent observations; Computer networks; Convergence; Kernel; Neural networks; Random variables;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Theory and Statistics, 1994. Proceedings., 1994 IEEE-IMS Workshop on
  • Conference_Location
    Alexandria, VA
  • Print_ISBN
    0-7803-2761-6
  • Type

    conf

  • DOI
    10.1109/WITS.1994.513898
  • Filename
    513898
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2345677