Model Identification for Engineering Variables

by Paul H. Wirsching, (A.M.ASCE), Assoc. Prof. of Aerospace and Mechanical Engrg.; Univ. of Arizona, Tucson, Ariz.,
James R. Carlson, Reliability Engr.; U.S. Army Troop Support Command, St. Louis, Mo.,

Serial Information: Journal of the Engineering Mechanics Division, 1977, Vol. 103, Issue 1, Pg. 125-138

Document Type: Journal Paper

Abstract: Presented herein are objective methods for determining the preferred statistical model to represent an engineering design variable. Given test data, two different criteria are proposed to indicate which of several competing statistical models, e.g., normal, lognormal, Weibull, EVD, etc., provides the best description of the variable. Using a Monte Carlo scheme, it was demonstrated that the proposed criteria is capable of a relatively high degree of discrimination with the power increasing with large samples. The results are useful in those applications of probabilistic design procedures where it is necessary to establish the best statistical description of stress or strength.

Subject Headings: Statistics | Data processing | Model tests | Probability | Field tests | Monte Carlo method | Europe | Monaco | Monte Carlo

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