Fisher information matrix for a four-parameter kappa distribution

  • Park J
  • Yoon Kim T
  • 3

    Readers

    Mendeley users who have this article in their library.
  • 6

    Citations

    Citations of this article.

Abstract

In this paper, the exact form of Fisher information matrix for a four-parameter kappa distribution (K4D) is determined. The K4D is so general that includes a variety of distributions as special cases. The necessary condition for the existence of Fisher information matrix is { 0 < h < frac(1, 2), k < frac(1, 2) } ∪ { h < 0, frac(1, 2) h < k < frac(1, 2) } for k ≠ 0, h ≠ 0. © 2007 Elsevier B.V. All rights reserved.

Author-supplied keywords

  • Beta function
  • Digamma function
  • Extreme value distribution
  • Hydrology
  • Maximum likelihood estimation

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • Jeong Soo Park

  • Tae Yoon Kim

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free