Explicit Gaussian Variational Approximation for the Poisson Lognormal Mixed Model
Mathematics, 2022 In recent years, the Poisson lognormal mixed model has been frequently used in modeling count data because it can accommodate both the over-dispersion of the data and the existence of within-subject correlation.
Xiaoping Shi +2 more
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Multivariate Poisson lognormal distribution for modeling counts from modern biological data: An overview [PDF]
Computational and Structural Biotechnology JournalModern biological data are often multivariate discrete counts, and there has been a dearth of statistical distributions to directly model such counts in an efficient manner.
Sanjeena Subedi, Utkarsh J Dang
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The Poisson-Lognormal Model as a Versatile Framework for the Joint Analysis of Species Abundances [PDF]
Frontiers in Ecology and Evolution, 2021 Joint Species Distribution Models (JSDM) provide a general multivariate framework to study the joint abundances of all species from a community. JSDM account for both structuring factors (environmental characteristics or gradients, such as habitat type ...
Julien Chiquet +3 more
doaj +4 more sources
Effects of road network characteristics on bicycle safety: A multivariate Poisson-lognormal model
Multimodal Transportation, 2022 Although cycling has benefits to environment and physical health, bicyclists are vulnerable road users. Prior studies have identified the environment, traffic and road user factors that affect the risk of bicycle-related crashes. However, it is rare that
Hongliang Ding, N N Sze
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Multivariate poisson-lognormal model for modeling related factors in crash frequency by severity
International Journal of Environmental Health Engineering, 2013 Aims: Traditionally, roadway safety analyses have used univariate distributions to model crash data for each level of severity separately. This paper uses the multivariate Poisson lognormal (MVPLN) models to estimate the expected crash frequency by two ...
Mehdi Tazhibi +3 more
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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 ...
Jianming Ma +2 more
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Joint Modeling of Mixed Plasmodium Species Infections Using a Bivariate Poisson Lognormal Model. [PDF]
Am J Trop Med Hyg, 2018 Infectious diseases often present as coinfections that may affect each other in positive or negative ways. Understanding the relationship between two coinfecting pathogens is thus important to understand the risk of infection and burden of disease caused by each pathogen.
Colborn KL, Mueller I, Speed TP.
europepmc +4 more sources
A Bayesian Poisson-lognormal Model for Count Data for Multiple-Trait Multiple-Environment Genomic-Enabled Prediction [PDF]
G3: Genes, Genomes, Genetics, 2017 When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account ...
Osval A. Montesinos-López +7 more
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SPLANG—a synthetic poisson-lognormal-based abundance and network generative model for microbial interaction inference algorithms [PDF]
Scientific ReportsMicrobes are pervasive and their interaction with each other and the environment can impact fields as diverse as health and agriculture. While network inference and related algorithms that use abundance data from pyrosequencing can infer microbial ...
Weicheng Qian +4 more
doaj +2 more sources
Algorithms, 2021 In this article we present a class of mixed Poisson regression models with varying dispersion arising from non-conjugate to the Poisson mixing distributions for modelling overdispersed claim counts in non-life insurance.
George Tzougas, Natalia Hong, Ryan Ho
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