Journal ArticleDOI
Multivariate Poisson regression with covariance structure
TLDR
In order to enlarge the applicability of the model, inference for a multivariate Poisson model with larger structure is proposed, i.e. different covariance for each pair of variables, and extension to models with complete structure with many multi-way covariance terms is discussed.Abstract:
In recent years the applications of multivariate Poisson models have increased, mainly because of the gradual increase in computer performance. The multivariate Poisson model used in practice is based on a common covariance term for all the pairs of variables. This is rather restrictive and does not allow for modelling the covariance structure of the data in a flexible way. In this paper we propose inference for a multivariate Poisson model with larger structure, i.e. different covariance for each pair of variables. Maximum likelihood estimation, as well as Bayesian estimation methods are proposed. Both are based on a data augmentation scheme that reflects the multivariate reduction derivation of the joint probability function. In order to enlarge the applicability of the model we allow for covariates in the specification of both the mean and the covariance parameters. Extension to models with complete structure with many multi-way covariance terms is discussed. The method is demonstrated by analyzing a real life data set.read more
Citations
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Journal ArticleDOI
The Effects of Traditional and Social Earned Media on Sales: A Study of a Microlending Marketplace
Andrew T. Stephen,Jeff Galak +1 more
TL;DR: In this article, the authors examine how two types of earned media, traditional (e.g., publicity and press mentions) and online community posts, affect sales and activity in each other.
Journal ArticleDOI
The Effects of Traditional and Social Earned Media on Sales: A Study of a Microlending Marketplace
Andrew T. Stephen,Jeff Galak +1 more
TL;DR: In this article, the authors examine how two types of earned media, traditional (e.g., publicity and press mentions) and social (e., blog and online community posts), affect sales and activity in each other, and find that both traditional and social earned media affect sales.
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.
Journal ArticleDOI
Collision prediction models using multivariate Poisson-lognormal regression
Karim El-Basyouny,Tarek Sayed +1 more
TL;DR: A new multivariate hazardous location identification technique is introduced, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature, and an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency is presented.
Journal ArticleDOI
What messages to post? Evaluating the popularity of social media communications in business versus consumer markets ☆
TL;DR: In this paper, the authors investigated the key factors that contribute to Facebook brand content popularity metrics (i.e., number of likes and comments) for Fortune 500 companies' brand posts in B2B versus business-to-consumer (B2C) markets.
References
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Book
An introduction to the bootstrap
Bradley Efron,Robert Tibshirani +1 more
TL;DR: This article presents bootstrap methods for estimation, using simple arguments, with Minitab macros for implementing these methods, as well as some examples of how these methods could be used for estimation purposes.
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Markov Chain Monte Carlo in Practice
TL;DR: The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.
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Journal ArticleDOI
The calculation of posterior distributions by data augmentation
Martin A. Tanner,Wing Hung Wong +1 more
TL;DR: If data augmentation can be used in the calculation of the maximum likelihood estimate, then in the same cases one ought to be able to use it in the computation of the posterior distribution of parameters of interest.