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Asymptotically Efficient Estimation of a Bivariate Gaussian–Weibull Distribution and an Introduction to the Associated Pseudo-truncated Weibull

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  • Steve P. Verrill
  • James W. Evans
  • David E. Kretschmann
  • Cherilyn A. Hatfield

Abstract

Two important wood properties are stiffness (modulus of elasticity or MOE) and bending strength (modulus of rupture or MOR). In the past, MOE has often been modeled as a Gaussian and MOR as a lognormal or a two or three parameter Weibull. It is well known that MOE and MOR are positively correlated. To model the simultaneous behavior of MOE and MOR for the purposes of wood system reliability calculations, we introduce a bivariate Gaussian–Weibull distribution and the associated pseudo-truncated Weibull. We use asymptotically efficient likelihood methods to obtain an estimator of the parameter vector of the bivariate Gaussian–Weibull, and then obtain the asymptotic distribution of this estimator.

Suggested Citation

  • Steve P. Verrill & James W. Evans & David E. Kretschmann & Cherilyn A. Hatfield, 2015. "Asymptotically Efficient Estimation of a Bivariate Gaussian–Weibull Distribution and an Introduction to the Associated Pseudo-truncated Weibull," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(14), pages 2957-2975, July.
  • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:14:p:2957-2975
    DOI: 10.1080/03610926.2013.805626
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    Cited by:

    1. Hanan Haj Ahmad & Ehab M. Almetwally & Dina A. Ramadan, 2023. "Investigating the Relationship between Processor and Memory Reliability in Data Science: A Bivariate Model Approach," Mathematics, MDPI, vol. 11(9), pages 1-23, May.

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