Modeling over-dispersed crash data with a long tail: Examining the accuracy of the dispersion parameter in negative binomial models
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منابع مشابه
Does the Dispersion Parameter of Negative Binomial Models Truly Estimate the Level of Dispersion in Over-dispersed Crash data with a Long Tail?
Despite many statistical models that have been proposed for modeling motor vehicle crashes, the most commonly used statistical tool remains the Negative binomial (NB) model. Crash data collected for safety studies may exhibit over-dispersion and a long tail (i.e., a few sites have unusually high number of crashes). However, some studies have shown that NB models cannot handle over-dispersed cou...
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Crash data can often be characterized by over-dispersion, heavy (long) tail and many observations with the value zero. Over the last few years, a small number of researchers have started developing and applying novel and innovative multi-parameter models to analyze such data. These multi-parameter models have been proposed for overcoming the limitations of the traditional negative binomial (NB)...
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There has been a considerable amount of work devoted by transportation safety analysts to the development and application of new and innovative models for analyzing crash data. One important characteristic about crash data that has been documented in the literature is related to datasets that contained a large amount of zeros and a long or heavy tail (which creates highly dispersed data). For s...
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Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...
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The objective of this study is to improve the estimation of the dispersion parameter of the negative binomial distribution for modeling motor vehicle collisions. The negative binomial distribution is widely used to model count data such as traffic crash data, which often exhibit low sample mean values and small sample sizes. Under such situations, the most commonly used methods for estimating t...
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تاریخ انتشار 2014