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Non-parametric least square estimation of distribution function

Non-parametric least square estimation of distribution function By using the non-parametric least square method, the strong consistent estimations of distribution function and failure function are established, where the distribution function F(x) after logist transformation is assumed to be approximated by a polynomial. The performance of simulation shows that the estimations are highly satisfactory. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Mathematics-A Journal of Chinese Universities Springer Journals

Non-parametric least square estimation of distribution function

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Publisher
Springer Journals
Copyright
Copyright © 2002 by Editorial Committee of Applied Mathematics-A Journal of Chinese Universities
Subject
Mathematics; Mathematics, general; Applications of Mathematics
ISSN
1005-1031
eISSN
1993-0445
DOI
10.1007/s11766-996-0009-0
Publisher site
See Article on Publisher Site

Abstract

By using the non-parametric least square method, the strong consistent estimations of distribution function and failure function are established, where the distribution function F(x) after logist transformation is assumed to be approximated by a polynomial. The performance of simulation shows that the estimations are highly satisfactory.

Journal

Applied Mathematics-A Journal of Chinese Universities – Springer Journals

Published: Jun 26, 1996

References

  • An extension of the moment method in statistical estimation
    Soong, T. T.
  • Method of moments with unknown distribution form
    Zheng, Zukang
  • Advanced Mathematical Statistics
    Chen, Xiru