Depth-based hotspot identification and multivariate ranking using the full Bayes approach

Accident Analysis and Prevention

Volumn 50, Issue , 2013, Pages 1082-1089

  El Basyouny, Karim ^a   Sayed, Tarek ^b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Full Bayes; Markov Chains Monte Carlo; Multivariate identification of hotspots; Multivariate Poisson log normal regression; Multivariate ranking; Performance evaluation criteria; Sensitivity; Specificity; Statistical depth functions

Indexed keywords

FULL BAYES; HOTSPOTS; LOG-NORMAL; MONTE CARLO; MULTIVARIATE RANKING; PERFORMANCE EVALUATION CRITERIA; SENSITIVITY; SPECIFICITY;

MARKOV PROCESSES; POISSON DISTRIBUTION;

MONTE CARLO METHODS;

ARTICLE; BAYES THEOREM; CANADA; HUMAN; METHODOLOGY; MONTE CARLO METHOD; POISSON DISTRIBUTION; PROBABILITY; RISK ASSESSMENT; SAFETY; SENSITIVITY AND SPECIFICITY; STATISTICS; TRAFFIC ACCIDENT;

ACCIDENTS, TRAFFIC; BAYES THEOREM; BRITISH COLUMBIA; HUMANS; MARKOV CHAINS; MONTE CARLO METHOD; POISSON DISTRIBUTION; RISK ASSESSMENT; SAFETY; SENSITIVITY AND SPECIFICITY;

EID: 84870290210     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2012.08.019     Document Type: Article

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