Goodness–of–Fit Tests for Birnbaum–Saunders Distributions

Authors

Department of Statistics, Yazd University Yazd, Iran.

10.29252/jirss.18.2.1

Abstract

  Goodness-of-fit tests are constructed for the two-parameter Birnbaum-Saunders distribution in the case where the parameters are unknown and therefore are estimated from the data. In each test, the procedure starts by computing efficient estimators of the parameters. Then the data are transformed by a normal transformation and normality tests are applied on the transformed data, thereby avoiding reliance on parametric asymptotic critical values or the need for bootstrap computations. Three classes of tests are considered, the first class being the classical tests based on the empirical distribution function, while the other class utilizes the empirical characteristic function and the final class utilizes the Kullback-Leibler information function. All methods are extended to cover the case of generalized three-parameter Birnbaum-Saunders distributions.

Keywords

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Volume 18, Issue 2
December 2019
Pages 1-20
  • Receive Date: 23 July 2022
  • Revise Date: 13 May 2024
  • Accept Date: 23 July 2022