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http://dx.doi.org/10.7734/COSEIK.2019.32.1.55

Development of MKDE-ebd for Estimation of Multivariate Probabilistic Distribution Functions  

Kang, Young-Jin (Research Institute of Mechanical Technology, Pusan Nat'l Univ.)
Noh, Yoojeong (School of Mechanical Engineering, Pusan Nat'l Univ.)
Lim, O-Kaung (School of Mechanical Engineering, Pusan Nat'l Univ.)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.32, no.1, 2019 , pp. 55-63 More about this Journal
Abstract
In engineering problems, many random variables have correlation, and the correlation of input random variables has a great influence on reliability analysis results of the mechanical systems. However, correlated variables are often treated as independent variables or modeled by specific parametric joint distributions due to difficulty in modeling joint distributions. Especially, when there are insufficient correlated data, it becomes more difficult to correctly model the joint distribution. In this study, multivariate kernel density estimation with bounded data is proposed to estimate various types of joint distributions with highly nonlinearity. Since it combines given data with bounded data, which are generated from confidence intervals of uniform distribution parameters for given data, it is less sensitive to data quality and number of data. Thus, it yields conservative statistical modeling and reliability analysis results, and its performance is verified through statistical simulation and engineering examples.
Keywords
correlated data; multivariate kernel density estimation; multivariate kernel density estimation with estimated bounded data; nonparametric statistical method; relative root mean squared error; reliability analysis;
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Times Cited By KSCI : 1  (Citation Analysis)
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