A fit of a mixture of bivariate normals to lumber stiffness : strength data / Steve P. Verrill [and three others].

Format:

Book

Government document

Author/Creator:

Verrill, S. P., author.

Contributor:

Forest Products Laboratory (U.S.), issuing body.

Series:

Research paper FPL-RP ; 696.

Research paper FPL-RP ; 696

Language:

English

Subjects (All):

Distribution (Probability theory).

Lumber--Mechanical properties--Statistics.

Lumber.

Lumber--Elastic properties.

Lumber--Fracture.

Statistics--Simulation methods.

Statistics.

Weibull distribution.

Gaussian distribution.

distribution (statistics-related concept).

Genre:

Statistics.

Physical Description:

1 online resource (23, [21] pages) : illustrations (some color).

Place of Publication:

Madison, WI : United States Department of Agriculture, Forest Service, Forest Products Laboratory, 2018.

Summary:

It has been common practice to assume that a two-parameter Weibull probability distribution is suitable for modeling lumber strength properties. In a series of papers published from 2012 to 2018, Verrill et al. demonstrated theoretically and empirically that the modulus of rupture (MOR) distribution of a visual grade of lumber or of lumber that has been "binned" by modulus of elasticity (MOE) is not a two parameter Weibull. Instead, the tails of the MOR distribution are thinned via "pseudo-truncation." The theoretical portion of Verrill et al.'s argument was based on the assumption of a bivariate normal--Weibull MOE--MOR distribution for the full ("mill run") population of lumber. Verrill et al. felt that it was important to investigate this assumption. In a recent pair of papers, they reported results obtained from a sample of size 200 drawn from a mill run population. They found that normal, lognormal, three-parameter beta, and Weibull distributions did not fit the sample MOR distribution of these data. Instead, it appeared that the MOR data might be fit by a skew normal distribution or a mixture of two univariate normals. In this paper, we investigate whether the joint MOE--MOR data from Verrill et al.'s recent mill run study can be well modeled as a mixture of two bivariate normals.

Notes:

"August 2018."

Includes bibliographical references (page 10).

Description based on online resource, PDF version; title from cover (USFS, viewed March 18, 2019).

Other Format:

Print version: Verrill, S. P. Fit of a mixture of bivariate normals to lumber stiffness.

OCLC:

1090069420