A semiparametric negative binomial generalized linear model for modeling over-dispersed count data with a heavy tail: Characteristics and applications to crash data.

No abstract is available for this paper.

• Publication year

  2016

• Venue

  Accident Analysis and Prevention

• Publication date

  2016-06-01

• Fields of study

  Mathematics, Medicine

• Identifiers
  DOI 10.1016/j.aap.2016.02.020PMID 26945472
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Statistical and econometric methods for transportation data analysis, Third Edition SimonWashington, MatthewKarlaftis, FredMannering, PanagiotisAnastasopoulis, 2020Chapman Hall/CRC Press, 496 pp., ISBN 978‐0‐367‐19902‐9

  2021cited by this paper
• Modeling the Association Between Clusters of SNPs and Disease Responses

  2015cited by this paper
• Modeling over-dispersed crash data with a long tail: Examining the accuracy of the dispersion parameter in Negative Binomial models

  2015cited by this paper
• Exploring the application of the negative binomial-generalized exponential model for analyzing traffic crash data with excess zeros

  2015cited by this paper
• Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach

  2014cited by this paper
• ABC model choice via random forests

  2014cited by this paper
• Analytic methods in accident research: Methodological frontier and future directions

  2014cited by this paper
• A caution about using deviance information criterion while modeling traffic crashes

  2014cited by this paper
• Speeding Up MCMC by Efficient Data Subsampling

  2014cited by this paper
• Applying the Generalized Waring model for investigating sources of variance in motor vehicle crash analysis.

  2014influential reference
• The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models

  2014cited by this paper
• Clustering in linear mixed models with approximate Dirichlet process mixtures using EM algorithm

  2013cited by this paper
• Asymptotically Exact, Embarrassingly Parallel MCMC

  2013cited by this paper
• The negative binomial-Lindley generalized linear model: characteristics and application using crash data.

  2012influential reference
• Negative Binomial Regression: Index

  2011cited by this paper
• Posterior Simulation of Normalized Random Measure Mixtures

  2011cited by this paper
• Slice sampling mixture models

  2011cited by this paper
• A SEMIPARAMETRIC BAYESIAN APPROACH TO NETWORK MODELLING USING DIRICHLET PROCESS PRIOR DISTRIBUTIONS

  2010cited by this paper
• Bayesian Nonparametrics: An invitation to Bayesian nonparametrics

  2010cited by this paper
• Semiparametric Bayes hierarchical models with mean and variance constraints

  2010influential reference
• Bayesian Modeling of MPSS Data: Gene Expression Analysis of Bovine Salmonella Infection

  2010cited by this paper
• The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives

  2010cited by this paper
• Application of finite mixture models for vehicle crash data analysis.

  2009cited by this paper
• A Bayesian Semiparametric Item Response Model with Dirichlet Process Priors

  2009cited by this paper
• Tobit analysis of vehicle accident rates on interstate highways.

  2008cited by this paper
• Negative Binomial Regression

  2007cited by this paper
• Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models

  2007cited by this paper
• Variational inference for Dirichlet process mixtures

  2006cited by this paper
• Selecting exposure measures in crash rate prediction for two-lane highway segments.

  2004cited by this paper
• Negative binomial loglinear mixed models

  2003cited by this paper
• Validation of FHWA Crash Models for Rural Intersections: Lessons Learned

  2003influential reference
• Modeling Traffic Crash-Flow Relationships for Intersections: Dispersion Parameter, Functional Form, and Bayes Versus Empirical Bayes Methods

  2003cited by this paper
• Bayesian infinite mixture model based clustering of gene expression profiles

  2002cited by this paper
• Exact and approximate sum representations for the Dirichlet process

  2002cited by this paper
• Flexible Parametric Models for Long-tailed Patent Count Distributions

  2002cited by this paper
• Semiparametric regression for count data

  2002cited by this paper
• Approximate Bayesian computation in population genetics.

  2002cited by this paper
• Gibbs Sampling Methods for Stick-Breaking Priors

  2001cited by this paper
• Computing Nonparametric Hierarchical Models

  1998cited by this paper
• Estimating mixture of dirichlet process models

  1998cited by this paper
• Dirichlet Process Mixed Generalized Linear Models

  1997cited by this paper
• Bayesian Data Analysis.

  1996cited by this paper
• Bayesian Density Estimation and Inference Using Mixtures

  1995cited by this paper
• A CONSTRUCTIVE DEFINITION OF DIRICHLET PRIORS

  1991cited by this paper
• Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems

  1974cited by this paper
• Prior Distributions on Spaces of Probability Measures

  1974cited by this paper
• A Bayesian Analysis of Some Nonparametric Problems

  1973cited by this paper
• (www.interscience.wiley.com) DOI: 10.1002/sim.2666 Flexible random-effects models using Bayesian semi-parametric models: Applications to institutional comparisons

  year unknowninfluential reference

• Identification of freeway crash hotspots: A novel methodological framework using non-crash data for identification and errors reduction

  2026cites this paper
• Spatiotemporal trends in pedestrian crashes: Socio-demographic influence and built environment impacts

  2026cites this paper
• Can mid-block pedestrian signals (MPS) provide greater safety benefits than other mid-block pedestrian crossings?

  2025cites this paper
• Assessing the importance of functional form selection in developing calibration functions for the highway safety manual predictive models.

  2025cites this paper
• Generalized Sichel Crash Predictive Model: Analysis of Predictor-Dependent Overdispersion in Urban Signalized Intersections

  2025cites this paper
• Examining the impact of centerline rumble strips on reducing rural two-lane head-on collisions in Maine.

  2025cites this paper
• Comparative evaluation of alternative Bayesian semi-parametric spatial crash frequency models

  2025cites this paper
• Effects of a Habitat Integrity Gradient on the Diversity of Odonates in the Legal Amazonia Zone of the Brazilian State of Maranhão

  2025cites this paper
• Road safety studies at micro, meso, and macroscopic levels: A systematic review

  2025cites this paper
• Examining the long-term impact of COVID-19 travel restrictions on crash occurrence

  2025cites this paper
• Crash analysis for urban road networks: Semi-parametric spatial negative binomial models with monotonic constraints.

  2025cites this paper
• Assessing the Negative Binomial-Lindley model for crash hotspot identification: Insights from Monte Carlo simulation analysis.

  2024cites this paper
• A comprehensive multi-objective framework for the estimation of crash frequency models.

  2024cites this paper
• The negative Binomial-Lindley model with Time-Dependent Parameters: Accounting for temporal variations and excess zero observations in crash data.

  2024cites this paper
• Incorporating Inconsistency Patterns on Road Networks into Crash Modeling

  2024cites this paper
• Accident Rate Prediction Model for Urban Expressway Underwater Tunnel

  2023cites this paper
• Sensitivity analysis of Bayesian nonparametric spatial crash frequency models

  2023cites this paper
• A Poisson Lognormal-Lindley Model for Simultaneous Estimation of Multiple Crash-types: Application of Multivariate and Pooled Univariate Models

  2023cites this paper
• A Moment-assisted Approach for Improving Subsampling-based MLE with Large-scale data

  2023cites this paper
• Stacked supervised Poisson autoencoders-based soft-sensor for defects prediction in steelmaking process

  2023cites this paper
• Evaluating alternative variations of Negative Binomial–Lindley distribution for modelling crash data

  2022cites this paper
• Stacked Denoising Autoencoders Based Poisson Regression For Count Data Modeling

  2022cites this paper
• Finite mixture Negative Binomial-Lindley for modeling heterogeneous crash data with many zero observations.

  2022cites this paper
• Exploring the impact of seasonal weather factors on frequency of lane-departure crashes in Maine

  2022cites this paper
• Crash Prediction Models and Methodological Issues

  2022influential citation
• Statistical Analysis of Frequency and Severity of Lane Departure Crashes in Maine

  2022cites this paper
• Generalizing the Negative Binomial-Lindley Model for Accounting Subpopulation Heterogeneity in Crash Data Analysis

  2022cites this paper
• Grouped Random Parameters Negative Binomial-Lindley for Accounting Unobserved Heterogeneity in Crash Data with Preponderant Zero Observations

  2022cites this paper
• Extension of generalized Poisson log-linear regression models for analysing three-way contingency table: Application to malaria data

  2022cites this paper
• Development of Crash Prediction Models for Urban Road Segments Using Poisson Inverse Gaussian Regression

  2022cites this paper
• Attention-based Stacked Supervised Poisson Autoencoders for Defects Prediction in Casting-rolling process

  2022cites this paper
• Derivation of the Empirical Bayesian method for the Negative Binomial-Lindley generalized linear model with application in traffic safety.

  2022cites this paper
• Accident prediction modelling for expressways: a review

  2022cites this paper
• Identification of Two Molecular Subtypes of Hepatocellular Carcinoma Based on Dysregulated Immune LncRNAs

  2021cites this paper
• Incorporating accident liability into crash risk analysis: A multidimensional risk source approach.

  2021cites this paper
• Modelling Animal-Vehicle Collision Counts across Large Networks Using a Bayesian Hierarchical Model with Time-Varying Parameters

  2021cites this paper
• Investigation of the Factors Influencing the Crash Frequency in Expressway Tunnels: Considering Excess Zero Observations and Unobserved Heterogeneity

  2021cites this paper
• Crash–frequency modeling

  2021cites this paper
• Summary of crash-frequency and crash-severity models in highway safety

  2021cites this paper
• Use/Analysis of Crash Data and Underreporting of Crashes

  2021cites this paper
• Highway-rail grade crossings accident prediction using Zero Inflated Negative Binomial and Empirical Bayes method.

  2021cites this paper
• A comparative analysis of intersection hotspot identification: Fixed vs. varying dispersion parameters in negative binomial models

  2020cites this paper
• A simulation analysis to study the temporal and spatial aggregations of safety datasets with excess zero observations

  2020cites this paper
• Lock on Form Development of Active Transportation Safety Performance Functions in California Development of Active Transportation Safety Performance Functions in California Final Technical Report

  2020influential citation
• The Application of Non-Parametric Count Models for the Modeling of Female’s Accident Rates in Hamadan Province from 2009 to 2016

  2020cites this paper
• Mapping wind erosion hazard with regression-based machine learning algorithms

  2020cites this paper
• Bitcoin Price Prediction Using Time Series Analysis and Machine Learning Techniques

  2020cites this paper
• Analyzing Highway Safety Datasets: Simplifying Statistical Analyses from Sparse to Big Data

  2019cites this paper
• Characteristics-based heuristics to select a logical distribution between the Poisson-gamma and the Poisson-lognormal for crash data modelling

  2019cites this paper
• Roadway Traffic Collision Analysis and Mitigation Using Data Analytics and Microscopic Driver Behavioral Modeling

  2019influential citation
• Accident Analysis and Prevention

  2019cites this paper
• Identifying and Incorporating Driver Behavior Variables into Crash Prediction Models

  2019influential citation
• Comparison of confidence and prediction intervals for different mixed-Poisson regression models

  2019cites this paper
• Identifying Highly Correlated Variables Relating to the Potential Causes of Reportable Wisconsin Traffic Crashes

  2019cites this paper
• Incorporating behavioral variables into crash count prediction by severity: A multivariate multiple risk source approach.

  2019cites this paper
• Bayesian Poisson hierarchical models for crash data analysis: Investigating the impact of model choice on site-specific predictions.

  2018cites this paper
• Crash data modeling with a generalized estimator.

  2018cites this paper
• Applying a random parameters Negative Binomial Lindley model to examine multi-vehicle crashes along rural mountainous highways in Malaysia.

  2018cites this paper
• Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data

  2018influential citation
• A multivariate heterogeneous-dispersion count model for asymmetric interdependent freeway crash types

  2018cites this paper
• Chapter 12. Cross-Sectional Modelling

  2018cites this paper
• Safety Prediction with Datasets Characterised with Excess Zero Responses and Long Tails

  2018cites this paper
• A flexible discrete density random parameters model for count data: Embracing unobserved heterogeneity in highway safety analysis

  2018cites this paper
• Use of Bivariate Dirichlet Process Mixture Spatial Model to Estimate Active Transportation-Related Crash Counts

  2018influential citation
• A methodology to design heuristics for model selection based on the characteristics of data: Application to investigate when the Negative Binomial Lindley (NB-L) is preferred over the Negative Binomial (NB).

  2017cites this paper
• Some Generalized Linear Models for the Estimation of the Mean Frequency of Claims in Motor Insurance

  2017cites this paper
• Exploring the Need for Having Region-Specific Calibration Factors

  2017cites this paper
• Bayesian Nonparametric Dirichlet Process Mixture Modeling in Transportation Safety Studies

  2017cites this paper
• Exploring the Need for Region-Specific Calibration Factors

  2017cites this paper
• Finite mixture modeling approach for developing crash modification factors in highway safety analysis.

  2016cites this paper
• Bayesian nonparametric modeling in transportation safety studies: Applications in univariate and multivariate settings

  2016cites this paper
• SHARE @ Children's Mercy SHARE @ Children's Mercy

  year unknowncites this paper