Randomized trial demonstrates accident rate predictions using regression analysis, indicating effective predictive criteria.
Various accident frequency models have appeared in the literature which predict the distribution of future accidents based on the number of past accidents. This article presents a method for deriving such distributions using several predictive criteria. It is assumed that an individual's accident experience is a Poisson process with the parameter a linear function of criterion variables. An iterative weighted least-squares procedure is used to solve the system of maximum likelihood equations required for estimating this parameter and a large sample test procedure is illustrated. The tenability of the model is viewed in the light of actual data.
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Donald C. Weber (1971) studied this question.
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