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A Multivariate Poisson-Lognormal Regression Model for Prediction of Crash Counts by Severity, Using Bayesian Methods [PDF]
Accident Analysis and Prevention, 2008 Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in ...
Kara Kockelman, Paul Damien
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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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On Fitting the Poisson Lognormal Distribution to Species-Abundance Data
Biometrics, 1974An extension of MacArthur's "broken stick" model is proposed to explain why species abundances should be lognormally distributed. A method of fitting the compound Poisson lognormal distribution by maximum likelihood is described; a computer program is available for performing the calculations.
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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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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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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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Food Control, 2011
Abstract In modelling risk management strategies (i.e., acceptance sampling plans, statistical process control), two basic assumptions have been normally made: that the true concentration of microorganisms are log-normally distributed within a batch , and that the variance of the samples is the same for a little or highly contaminated lot .
Úrsula Gonzales Barrón, Francis Butler
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Abstract In modelling risk management strategies (i.e., acceptance sampling plans, statistical process control), two basic assumptions have been normally made: that the true concentration of microorganisms are log-normally distributed within a batch , and that the variance of the samples is the same for a little or highly contaminated lot .
Úrsula Gonzales Barrón, Francis Butler
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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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Maximum likelihood fitting of the Poisson lognormal distribution
Environmental and Ecological Statistics, 2007In this paper some properties and analytic expressions regarding the Poisson lognormal distribution such as moments, maximum likelihood function and related derivatives are discussed. The author provides a sharp approximation of the integrals related to the Poisson lognormal probabilities and analyzes the choice of the initial values in the fitting ...
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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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