A Horvitz-type estimation on incomplete traffic accident data analyzed via a zero-inflated Poisson model

Martin T. Lukusa, Frederick Kin Hing Phoa

研究成果: Article

1 引文 斯高帕斯(Scopus)

摘要

To improve the road safety, policy makers relay on data analysis to enact new traffic policies. Accordingly, statistical modeling has been linked in various studies of road crash counts with excess zeros. On top of this excess zero problem, missing data are also likely to occur in the road traffic accident data. Unless the missing data are resulted randomly, the popular naive estimation may not provide reliable results for policy making. In contrast, the implementation of the Horvitz method, which inversely weights the observed data by a weight that are obtained parametrically or nonparametrically, results in reliable estimators. We received satisfactory results on the performance of our approach handling the missing data problems in both a Monte Carlo simulation and a real traffic accident data exploration.

原文English
文章編號105235
期刊Accident Analysis and Prevention
134
DOIs
出版狀態Published - 2020 一月

All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Safety, Risk, Reliability and Quality
  • Public Health, Environmental and Occupational Health

指紋 深入研究「A Horvitz-type estimation on incomplete traffic accident data analyzed via a zero-inflated Poisson model」主題。共同形成了獨特的指紋。

  • 引用此