在面板数据中适应系统和未观察到的异质性:应用于宏观层面的崩溃建模

IF 12.5 1区 工程技术 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Tanmoy Bhowmik , Shamsunnahar Yasmin , Naveen Eluru
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引用次数: 4

摘要

当前的研究通过提出一种混合模型框架(a)将未观察到的异质性纳入简约的框架,(b)允许额外的灵活性来适应观察到的/系统的异质性,从而为多元模型的新兴文献做出了贡献。具体来说,我们估计了一个潜在分割面板混合负二项(LPMNB)模型来研究不同崩溃类型的区域级崩溃计数。此外,我们将提出的混合LPMNB模型与面板混合负二项模型(PMNB)进行了比较,PMNB通过模拟设置适应未观察到的异质性。考虑到一组综合的外生变量,分析使用了2016年佛罗里达中部地区不同崩溃类型的区域性崩溃记录。通过计算几个拟合优度指标以及弹性分析,进一步增强了比较练习,LPMNB模型提供的结果突出了所提出模型的价值。此外,为了提供结合计算复杂性维度和其他重要属性的模型选择的见解,我们进行了权衡分析,考虑了四个不同的属性:(a)模型拟合,(b)预测,(c)推理能力和(d)计算复杂性;跨越六种不同的模型结构,包括传统的碰撞频率模型和我们提出的LPMNB模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accommodating for systematic and unobserved heterogeneity in panel data: Application to macro-level crash modeling

The current research contributes to the burgeoning literature on multivariate models by proposing a hybrid model framework that (a) incorporates unobserved heterogeneity in a parsimonious framework and (b) allows for additional flexibility to accommodate for observed/systematic heterogeneity. Specifically, we estimate a Latent Segmentation Panel Mixed Negative Binomial (LPMNB) model to study the zonal level crash counts across different crash types. Further, we undertake a comparison exercise of the proposed hybrid LPMNB model with a Panel Mixed Negative Binomial model (PMNB) that accommodates for unobserved heterogeneity via a simulation setting. The analysis is conducted using the zonal level crash records by different crash types from Central Florida region for the year 2016 considering a comprehensive set of exogenous variables. The comparison exercise is further augmented by computing several goodness of fit measures along with elasticity analysis and the results offered by the LPMNB model highlight the value of the proposed model. Further, to offer insights on model selection incorporating computational complexity dimension along with other important attributes, we conduct a trade-off analysis considering four different attributes: (a) model fit, (b) prediction, (c) inference power and (d) computational complexity; across six different model strictures including traditional crash frequency models and our proposed LPMNB model.

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来源期刊
CiteScore
22.10
自引率
34.10%
发文量
35
审稿时长
24 days
期刊介绍: Analytic Methods in Accident Research is a journal that publishes articles related to the development and application of advanced statistical and econometric methods in studying vehicle crashes and other accidents. The journal aims to demonstrate how these innovative approaches can provide new insights into the factors influencing the occurrence and severity of accidents, thereby offering guidance for implementing appropriate preventive measures. While the journal primarily focuses on the analytic approach, it also accepts articles covering various aspects of transportation safety (such as road, pedestrian, air, rail, and water safety), construction safety, and other areas where human behavior, machine failures, or system failures lead to property damage or bodily harm.
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