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Small Sample Properties of Asymptotically Efficient Estimators of the Parameters of a Bivariate Gaussian–Weibull Distribution

Year:

2012

Publication type:

Research Paper (RP)

Primary Station(s):

Forest Products Laboratory

Source:

USDA Forest Service, Forest Products Laboratory, Research Paper, FPL-RP-667, 2012: 48 p.

Description

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, in a 2012 paper Verrill, Evans, Kretschmann, and Hatfield introduced a bivariate Gaussian–Weibull distribution and the associated pseudo-truncated Weibull. In that paper, they obtained asymptotically efficient estimators of the parameter vector of the bivariate Gaussian–Weibull. In this paper, we discuss computer simulations that investigated the small sample properties of these parameter estimates. We also discuss a Web-based computer program that obtains these estimates.

Citation

Verrill, Steve P.; Evans, James W.; Kretschmann, David E.; Hatfield, Cherilyn A. 2012. Small sample properties of asymptotically efficient estimators of the parameters of a bivariate Gaussian–Weibull distribution. Research Paper FPL-RP-667. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory. 46 p.

Cited

Publication Notes

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  • This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
https://www.fs.usda.gov/treesearch/pubs/41243