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http://dx.doi.org/10.7734/COSEIK.2019.32.3.173

Comparison Study of Kernel Density Estimation according to Various Bandwidth Selectors  

Kang, Young-Jin (Research Institute of Mechanical Technology, Pusan Nat'l Univ.)
Noh, Yoojeong (School of Mechanical Engineering, Pusan Nat'l Univ.)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.32, no.3, 2019 , pp. 173-181 More about this Journal
Abstract
To estimate probabilistic distribution function from experimental data, kernel density estimation(KDE) is mostly used in cases when data is insufficient. The estimated distribution using KDE depends on bandwidth selectors that smoothen or overfit a kernel estimator to experimental data. In this study, various bandwidth selectors such as the Silverman's rule of thumb, rule using adaptive estimates, and oversmoothing rule, were compared for accuracy and conservativeness. For this, statistical simulations were carried out using assumed true models including unimodal and multimodal distributions, and, accuracies and conservativeness of estimating distribution functions were compared according to various data. In addition, it was verified how the estimated distributions using KDE with different bandwidth selectors affect reliability analysis results through simple reliability examples.
Keywords
bandwidth selector; kernel density estimation; multimodal distribution; reliability analysis; statistical modeling; unimodal distribution;
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