Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Determination of Expected Frequency01:08

Determination of Expected Frequency

1.7K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
1.7K
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

6.3K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
6.3K
Introduction to Test of Independence01:21

Introduction to Test of Independence

2.1K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.1K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.3K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

707
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
707
Random Variables01:09

Random Variables

14.7K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
14.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Multi-scale modeling of electric vehicle fatal crash risk: uncovering spatial heterogeneity and infrastructure-land use coupling mechanisms.

Accident; analysis and prevention·2026
Same author

Modeling driver lane-changing aggressiveness under target-lane interference: A Bayesian approach using naturalistic trajectory data.

Accident; analysis and prevention·2026
Same author

Local inhibition of glaucomatous mitochondrial dysfunction using an engineered annular sector microneedle.

Biomaterials·2026
Same author

The nonlinear impact of road safety policy implementation on the severity of road traffic crashes: A fusion of deep learning and Bayesian random parameter methods.

Accident; analysis and prevention·2026
Same author

An exploration of active safety technologies for commercial vehicles: Research on on-road driving risk identification based on alarm and other multi-source data.

Accident; analysis and prevention·2026
Same author

A comparison of collision factors and toxicologic characteristics for rural and urban drivers presenting to the emergency department after a vehicular collision.

Traffic injury prevention·2026

Related Experiment Video

Updated: Apr 29, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

9.9K

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash

Chunjiao Dong1, David B Clarke1, Xuedong Yan2

  • 1Center for Transportation Research, The University of Tennessee, 600 Henley Street, Knoxville, TN 37996, USA.

Accident; Analysis and Prevention
|May 21, 2014
PubMed
Summary

A new multivariate random-parameters zero-inflated negative binomial (MRZINB) model improves crash frequency analysis. This advanced model better identifies high-risk roadway factors at intersections, enhancing traffic safety strategies.

Keywords:
Crash frequencyFull Bayesian methodGeometric designMRZINB model

More Related Videos

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

18.1K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Related Experiment Videos

Last Updated: Apr 29, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

9.9K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

18.1K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Area of Science:

  • Transportation Engineering
  • Traffic Safety Analysis
  • Statistical Modeling

Background:

  • Crash data analysis is crucial for identifying high-risk road situations and developing safety countermeasures.
  • Existing methods struggle with correlated multivariate crash data, especially with excess zero counts.
  • Understanding relationships between crash frequencies and roadway variables requires advanced statistical approaches.

Purpose of the Study:

  • To introduce and evaluate a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts.
  • To analyze crash frequencies at urban signalized intersections in Tennessee using the MRZINB model.
  • To compare the performance of MRZINB with the multivariate zero-inflated negative binomial (MZINB) model.

Main Methods:

  • Development of a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model.
  • Application of the full Bayesian method for model parameter estimation.
  • Analysis of crash data integrated with road inventory, pavement conditions, traffic factors, and geometric design features for urban signalized intersections.

Main Results:

  • The MRZINB model identified additional statistically significant factors influencing crash frequencies compared to the MZINB model.
  • The MRZINB model demonstrated a better goodness of fit in establishing relationships between crash frequencies and roadway variables.
  • Empirical results confirmed the MRZINB model's ability to accommodate unobserved heterogeneity and excess zero counts in correlated crash data.

Conclusions:

  • The MRZINB regression model is a powerful tool for jointly modeling correlated crash counts, offering superior performance over traditional models.
  • The model's random parameters significantly vary across intersections for different crash types, highlighting intersection-specific risk factors.
  • The MRZINB model provides a robust framework for enhancing traffic safety analysis and developing targeted countermeasures.