A multi-criterion evaluation approach to selection of the best statistical distribution
Using traditional statistical analysis methods one chooses a hypothesized distribution to describe the observed data, estimate the distribution parameters, and then apply the Kolmogorov-Smimov test statistic D-max or the Chi-square test statistic chi(2) to test the goodness of fit. Observing that the D-max statistic considers only the information on cumulative distribution function (CDF), we have proposed a multi-criterion decision-making approach considering not only the D a., statistic but also the errors in CDF, the errors in probability distribution function, the errors in skewness and kurtosis, and experts' preference. A case study is provided to illustrate the use of the proposed approach. (C) 2004 Elsevier Ltd. All rights reserved.
statistics goodness of fit decision-making multicriterion hypothesis testing