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A new flexible skewed bimodal distribution with multivariate extensions: Theory and application to traffic crash injury severity analysis

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  • Bhat, Chandra R.

Abstract

This paper introduces a proper multivariate flexible continuous parametric distribution, a first to our knowledge, that allows for asymmetric bimodality in each univariate dimension. The distribution is developed through a combination of an approach to generate bimodality and a Yeo-Johnson (YJ)-based transformation. A number of properties of the proposed distribution are stated and proved, including a computationally easy way to generate random variates from the proposed multivariate density. An application of the proposed distribution is demonstrated to analyze injury severity using data drawn from the Texas Department of Transportation (TxDOT) crash database of two-vehicle crashes at intersections. The proposed distribution may be applied to a number of different econometric modeling contexts in both a univariate and multivariate context, and in a whole variety of fields to consider bimodal asymmetry in stochastic distributions.

Suggested Citation

  • Bhat, Chandra R., 2026. "A new flexible skewed bimodal distribution with multivariate extensions: Theory and application to traffic crash injury severity analysis," Transportation Research Part B: Methodological, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:transb:v:204:y:2026:i:c:s0191261525002139
    DOI: 10.1016/j.trb.2025.103364
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    References listed on IDEAS

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