Local logarithm partial likelihood estimation of panel count data model with an unknown link function

2021 ◽  
pp. 107346
Author(s):  
Yijun Wang ◽  
Weiwei Wang ◽  
Xiaobing Zhao
Keyword(s):  
Count Data ◽  
Data Model ◽  
Likelihood Estimation ◽  
Partial Likelihood ◽  
Panel Count Data ◽  
Link Function ◽  
Count Data Model ◽  
Unknown Link

scholarly journals Analysis of Road Traffic Accidents in the Punjab by Using Panel Count Data Models

2021 ◽  
Vol 3 (1) ◽  
pp. 1-13
Author(s):  
Muhammad Anus Hayat Khan ◽  
Ijaz Hussain
Keyword(s):  
Count Data ◽  
Data Model ◽  
Road Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Data Models ◽  
Panel Count Data ◽  
Road Traffic Accidents ◽  
Count Data Models ◽  
Count Data Model

Each year more than three thousand people die and get serious injuries in traffic accidents. Count data model provide more precise tools for planners and decision makers to conduct proactive road safety planning.We analyzed the exploratory research of Road Traffic Accidents (RTAs) and furthermore explores the factors affecting the RTAs frequency in 36 districts of the Punjab over a time period of three years (July 1, 2013 June 30, 2016) with monthly data using panel count data models. Among the models considered, the random parameters Poisson panel count data model is found to fit the data best. The exploratory analysis shows that highly dense populated districts with large number of registered vehicles causes more accidents as compared to low density populated districts. It is found that, most of the variables used to control the variation in the frequency of RTAs counts play vital role with higher significance levels. The application of regression analysis and modeling of RTAs at district level in Punjab will help to identification of districts with high RTAs rates and this could help more efficient road safety management in the Punjab.

Sign in / Sign up

Export Citation Format

Share Document