Comparison of distribution selection methods

Data-Driven Analysis for Facility Management in Higher Education Institution

“…To compare which distribution fits best to the data, a recent study analyzed the robustness of different methods of comparing fitted distributions such as AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), LRT (log-likelihood ration test), etc. [39]. AIC and BIC measure the performance of the models based on their complexity.…”

“…AIC is a prediction error estimator which prevents overfitting of data whereas BIC penalizes the model more based on the number of parameters. While comparing the AIC and BIC, lower scores are preferred and both information criteria are used for appropriate model selection, and it can also be used for distribution selection [39]. The Negative Binomial (NB) distribution for a discrete random variable (X) can be calculated based on Equation (2) [40]:…”

Differences by Selection Method for Exposure Factor Input Distribution for Use in Probabilistic Consumer Exposure Assessment

“…The BIC is preferred over the AIC when the sample size is much larger than number of parameters. 4) There are no clear guidelines for selecting the most appropriate statistical approaches of GOF test. 3) US EPA recommended decisions of the distribution function depending on the number of data point, the outcome of interest, and the tail of distribution.…”

“…7,8) Little research has been conducted on whether GOF test methods accurately recover the original input parameters. Nonetheless, Chiew et al 4) created mathematically ideal random distributions and compared the distributions derived from several GOF test methods. They found that the performance of statistical methods varied depending on the data characteristics.…”