Characteristics-based heuristics to select a logical distribution between the Poisson-gamma and the Poisson-lognormal for crash data modelling. Issue 2 (29th November 2019)

• Record Type: Journal Article
• Authors: Shirazi, Mohammadali; Lord, Dominique
• Is Part Of: Transportmetrica. Volume 15:Issue 2(2019)
• Page Start: 1791
• Page End: 1803
• Publication Date: 2019-11-29
• Subjects: Model selection -- characteristics based heuristics -- classification -- Poisson-gamma -- Poisson-lognormal; Transportation -- Periodicals; Transportation -- Research -- Periodicals; 388.072
• DOI: 10.1080/23249935.2019.1640313
• Languages: English
• ISSNs: 2324-9935

Abstract

Abstract : 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.