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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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Journal ArticleDOI
The Poisson-Weibull generalized linear model for analyzing motor vehicle crash data
TL;DR: The results of this study show that the PW GLM performs as well as the PG GLM in terms of goodness-of-fit statistics.
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A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data.
TL;DR: A semi-nonparametric Poisson regression model is developed to analyze motor vehicle crash frequency data collected from rural multilane highway segments in California, US to provide a better understanding of crash data structure through its ability to capture potential multimodality in the distribution of unobserved heterogeneity.
Journal ArticleDOI
Characterizing the performance of the Conway-Maxwell Poisson generalized linear model.
Royce A. Francis,Royce A. Francis,Srinivas Reddy Geedipally,Seth D. Guikema,Soma S. Dhavala,Dominique Lord,Sarah LaRocca +6 more
TL;DR: The results of the study indicate that the COM-Poisson GLM is flexible enough to model under-, equi-, and overdispersed data sets with different sample mean values, and yields accurate parameter estimates.
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Development of Accident Modification Factors for Rural Frontage Road Segments in Texas Using Generalized Additive Models
TL;DR: AMFs produced from GAMs are more flexible to characterize the safety effect of simultaneous changes in geometric and operational features than when independent AMFs are applied together and indicated a nonlinear relationship between crash risk and changes in lane and shoulder widths for frontage roads in Texas.
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Application of finite mixture models for analysing freeway incident clearance time
TL;DR: In this article, a g-component mixture model is applied to analyze incident clearance time. But, the model is not suitable for the analysis of large-scale data sets, and it cannot capture the effects of explanatory variables across different subpopulations.