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Multivariate Poisson Lognormal Modeling of Weather-Related Crashes on Freeways
Transportation Research Record: Journal of the Transportation Research Board, 2018Adverse weather conditions are one of the primary causes of motor vehicle crashes. To identify the factors contributing to crashes during adverse weather conditions and recommend cost-effective countermeasures, it is necessary to develop reliable crash prediction models to estimate weather-related crash frequencies.
Kai Wang, Shanshan Zhao, Eric Jackson
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On robustness of maximum likelihood estimates for Poisson-lognormal models
Statistics & Probability Letters, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Weems, K. S., Smith, P. J.
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Multivariate Poisson-Lognormal Models for Jointly Modeling Crash Frequency by Severity
Transportation Research Record: Journal of the Transportation Research Board, 2007A new multivariate approach is introduced for jointly modeling data on crash counts by severity on the basis of multivariate Poisson-lognormal models. Although the data on crash frequency by severity are multivariate in nature, they have often been analyzed by modeling each severity level separately, without taking into account correlations that exist
Eun Sug Park, Dominique Lord
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ANALYTICAL APPROXIMATION OF EXACT POISSON-LOGNORMAL LIKELIHOOD FUNCTIONS
Health Physics, 2008Simple analytical approximations of exact Poisson-lognormal likelihood functions are obtained numerically. The Poisson-lognormal statistical model describes counting measurements with lognormally distributed normalization factors. The analytical expressions for the likelihood function allow maximum likelihood data fitting using nonlinear-least-squares ...
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Methods for fitting the Poisson-lognormal distribution to microbial testing data
Food Control, 2012The Poisson distribution can be used to describe the number of microorganisms in a serving of food, but in most food-safety applications, the variability of the Poisson distribution is insufficient to describe the heterogeneity in microbial contamination across the population of all servings.
Michael S. Williams, Eric D. Ebel
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The spatio-temporal multivariate Poisson lognormal model
AIP Conference Proceedings, 2018To deal with the variation and correlation structure of accident data along with recognized covariate effects, we develop a spatio-temporal model for multivariate accident count data. Based on the multivariate Poisson lognormal model, we introduce linear combinations of random impulses to capture spatial correlation.
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Accident Analysis & Prevention, 2020
Reducing nonmotorized crashes requires a profound understanding of the causes and consequences of the crashes at the facility level. Generally, existing literature on bicyclists and pedestrian crash models suffers from two distinct problems: lack of exposure/volume data and inadequacy in capturing potential correlations across various crash aspects. To
Sirajum, Munira +2 more
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Reducing nonmotorized crashes requires a profound understanding of the causes and consequences of the crashes at the facility level. Generally, existing literature on bicyclists and pedestrian crash models suffers from two distinct problems: lack of exposure/volume data and inadequacy in capturing potential correlations across various crash aspects. To
Sirajum, Munira +2 more
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The poisson-lognormal model for bibliometric/scientometric distributions
Information Processing & Management, 1994Abstract The Poisson-lognormal model assumes that the intensity parameter of a Poisson process has a lognormal distribution in a sample of observations. This model can yield highly skewed, discrete distributions, but must be estimated by numerical methods.
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Food Control, 2011
Abstract The choice of statistical distributions characterising microbial counts is essential in risk assessment and risk management. While the lognormal distribution has been long used to directly model the microbial data obtained from food samples, it does not allow for complete absence of microorganisms in a sample.
Ursula Gonzales-Barron, Francis Butler
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Abstract The choice of statistical distributions characterising microbial counts is essential in risk assessment and risk management. While the lognormal distribution has been long used to directly model the microbial data obtained from food samples, it does not allow for complete absence of microorganisms in a sample.
Ursula Gonzales-Barron, Francis Butler
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Predicting future consumer purchases in grocery retailing with the condensed Poisson lognormal model
Journal of Retailing and Consumer Services, 2022Abstract To identify the effect of marketing actions on consumer purchasing, analysts must disentangle the dynamic component of purchasing from expected period-to-period stochastic fluctuations. This is done by comparing marketplace observations to the conditional expectation of future purchasing.
Giang Trinh, Malcolm J. Wright
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