Accounting for high-order correlations in probabilistic characterization of environmental variables,and evaluation |
| |
| Authors: | Kasemsan Manomaiphiboon Sun-Kyoung Park Armistead G Russell |
| |
| Institution: | (1) School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA;(2) Joint Graduate School of Energy and Environment (JGSEE), King Mongkut’s University of Technology Thonburi (KMUTT), 91 Prachauthit Rd., Bangmod, Tungkru, Bangkok, 10140, Thailand;(3) Present address: Transportation Department, North Central Texas Council of Governments, Arlington, TX, USA |
| |
| Abstract: | Probabilistic characterization of environmental variables or data typically involves distributional fitting. Correlations,
when present in variables or data, can considerably complicate the fitting process. In this work, effects of high-order correlations
on distributional fitting were examined, and how they are technically accounted for was described using two multi-dimensional
formulation methods: maximum entropy (ME) and Koehler–Symanowski (KS). The ME method formulates a least-biased distribution
by maximizing its entropy, and the KS method uses a formulation that conserves specified marginal distributions. Two bivariate
environmental data sets, ambient particulate matter and water quality, were chosen for illustration and discussion. Three
metrics (log-likelihood function, root-mean-square error, and bivariate Kolmogorov–Smirnov statistic) were used to evaluate
distributional fit. Bootstrap confidence intervals were also employed to help inspect the degree of agreement between distributional
and sample moments. It is shown that both methods are capable of fitting the data well and have the potential for practical
use. The KS distributions were found to be of good quality, and using the maximum likelihood method for the parameter estimation
of a KS distribution is computationally efficient. |
| |
| Keywords: | Bootstrap Goodness of fit High-order correlation Probability distribution Product moment |
| 本文献已被 SpringerLink 等数据库收录！ |
|
