Several studies have shown that the Poisson-lognormal (PLN) offers a better alternative compared to the Poisson-gamma (PG) when data are skewed while the PG is a more reliable option otherwise. However, it is not explicitly clear when the analyst needs to shift from the PG to the PLN – or vice versa. In addition, so far, the comparison has usually been accomplished using the goodness-of-fit statistics or statistical tests. Such metrics rarely give any intuitions into why a specific distribution or model is preferred over another. This paper addresses these topics by (1) designing characteristics-based heuristics to select a distribution between the PG and PLN, and (2) prioritizing the most important summary statistics to select a distribution between these two options. The results show that the kurtosis and percentage-of-zeros of data are among the most important summary statistics needed to distinguish between these two options.
Characteristics-based heuristics to select a logical distribution between the Poisson-gamma and the Poisson-lognormal for crash data modelling
Transportmetrica A: Transport Science ; 15 , 2 ; 1791-1803
2019-11-29
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Multivariate Poisson-Lognormal Models for Jointly Modeling Crash Frequency by Severity
Online Contents | 2007
|Multivariate Poisson-Lognormal Models for Jointly Modeling Crash Frequency by Severity
Transportation Research Record | 2007
|Bayesian Multivariate Poisson Lognormal Models for Crash Severity Modeling and Site Ranking
Transportation Research Record | 2009
|