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.


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    Titel :

    Characteristics-based heuristics to select a logical distribution between the Poisson-gamma and the Poisson-lognormal for crash data modelling


    Beteiligte:

    Erschienen in:

    Erscheinungsdatum :

    2019-11-29


    Format / Umfang :

    13 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch