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Dominique Lord
Researcher at Texas A&M University
Publications - 226
Citations - 12815
Dominique Lord is an academic researcher from Texas A&M University. The author has contributed to research in topics: Poison control & Crash. The author has an hindex of 46, co-authored 216 publications receiving 11248 citations. Previous affiliations of Dominique Lord include Ryerson University & University of Washington.
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Modeling motor vehicle crashes using Poisson-gamma models: examining the effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter.
TL;DR: In this article, a series of Poisson-gamma distributions were simulated using different values describing the mean, the dispersion parameter, and the sample size, and they were fitted to crash data collected in Toronto, Ont. characterized by a low sample mean and small sample size.
Modeling Motor Vehicle Crashes Using Poisson-Gamma Models: Examining Effects of Low Sample Mean Values and Small Sample Size on Estimation of Fixed Dispersion Parameter
TL;DR: The study shows that a low sample mean combined with a small sample size can seriously affect the estimation of the dispersion parameter, no matter which estimator is used within the estimation process.
Journal ArticleDOI
Multivariate Poisson-Lognormal Models for Jointly Modeling Crash Frequency by Severity
Eun Sug Park,Dominique Lord +1 more
TL;DR: In this paper, a new multivariate approach is introduced for jointly modeling data on crash counts by severity on the basis of multivariate Poisson-lognormal models, which can cope with both overdispersion and a fully general correlation structure in the data.
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Predicting motor vehicle crashes using Support Vector Machine models.
TL;DR: In this article, Support Vector Machine (SVM) models were used for predicting motor vehicle crashes. But, the results showed that SVM models do not overfit the data and offer similar, if not better, performance than Back-Propagation Neural Network (BPNN) models documented in previous research.
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Accident Prediction Models With and Without Trend: Application of the Generalized Estimating Equations Procedure
Dominique Lord,Bhagwant Persaud +1 more
TL;DR: An application is presented of a generalized estimating equations (GEE) procedure to develop an APM that incorporates trend in accident data and the GEE model incorporating the time trend is shown to be superior to models that do not accommodate trend and/or the temporal correlation in accidentData.