Research on the Yangtze River Accident Casualties Using Zero-inflated Negative Binomial Regression Technique

被引：0
|
作者：
Wang Hao [1 ]
Yang Ya-dong [1 ]
Ma Yong [1 ]
机构：
[1] Wuhan Univ Technol, Hubei Inland Shipping Technol Key Lab, Wuhan 430063, Peoples R China
来源：
2016 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION ENGINEERING (ICITE) | 2016年
关键词：
ZINB regression model; Casualty; Yangtze River water traffic; Parameter estimation; MODEL;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The zero-inflated negative binomial regression model is used to study the influencing factors of casualties in water traffic accidents. The Pearson correlation analysis is used to obtain the influencing factors of casualties in water traffic accidents. The ZINB regression model is established to evaluate the influencing factors. According to the analysis results, influencing factors are sorted in the ascending order of factor influencing value. This method has been applied in casualties in water traffic, which is in the area under administration of Yangtze River MSA (Maritime Safety Administration). And the results can provide the reference for the maritime management to make effective strategy to reduce the casualties in water traffic accidents.
引用
收藏
页码：72 / 75
页数：4
相关论文
共 50 条
  • [1] Evaluation of Shipping Accident Casualties using Zero-inflated Negative Binomial Regression Technique
    Weng, Jinxian
    Ge, Ying En
    Han, Hao
    [J]. JOURNAL OF NAVIGATION, 2016, 69 (02): : 433 - 448
  • [2] Combining zero-inflated negative binomial regression with MLRT techniques: An approach to evaluating shipping accident casualties
    Weng, Jinxian
    Yang, Dong
    Qian, Ting
    Huang, Zhi
    [J]. OCEAN ENGINEERING, 2018, 166 : 135 - 144
  • [3] A Zero-Inflated Negative Binomial Regression Model to Evaluate Ship Sinking Accident Mortalities
    Chai, Tian
    Xiong, De-qi
    Weng, Jinxian
    [J]. TRANSPORTATION RESEARCH RECORD, 2018, 2672 (11) : 65 - 72
  • [4] Modeling Tetanus Neonatorum case using the regression of negative binomial and zero-inflated negative binomial
    Amaliana, Luthfatul
    Sa'adah, Umu
    Wardhani, Ni Wayan Surya
    [J]. FIRST AHMAD DAHLAN INTERNATIONAL CONFERENCE ON MATHEMATICS AND MATHEMATICS EDUCATION, 2018, 943
  • [5] Bivariate zero-inflated negative binomial regression model with applications
    Faroughi, Pouya
    Ismail, Noriszura
    [J]. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2017, 87 (03) : 457 - 477
  • [6] An alternative bivariate zero-inflated negative binomial regression model using a copula
    So, Sunha
    Lee, Dong-Hee
    Jung, Byoung Cheol
    [J]. ECONOMICS LETTERS, 2011, 113 (02) : 183 - 185
  • [7] A score test for testing a zero-inflated Poisson regression model against zero-inflated negative binomial alternatives
    Ridout, M
    Hinde, J
    Demétrio, CGB
    [J]. BIOMETRICS, 2001, 57 (01) : 219 - 223
  • [8] The zero-inflated negative binomial regression model with correction for misclassification: an example in caries research
    Mwalili, Samuel M.
    Lesaffre, Emmanuel
    Declerck, Dominique
    [J]. STATISTICAL METHODS IN MEDICAL RESEARCH, 2008, 17 (02) : 123 - 139
  • [9] On estimation and influence diagnostics for zero-inflated negative binomial regression models
    Garay, Aldo M.
    Hashimoto, Elizabeth M.
    Ortega, Edwin M. M.
    Lachos, Victor H.
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2011, 55 (03) : 1304 - 1318
  • [10] On estimation and influence diagnostics for zero-inflated negative binomial regression models
    Departamento de Estatstica, Universidade Estatual de Campinas, Brazil
    不详
    不详
    [J]. Comput. Stat. Data Anal., 3 (1304-1318):
12345