Journal ArticleDOI
Finite mixtures of multivariate Poisson distributions with application
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TLDR
In this article, the authors examined finite mixtures of multivariate Poisson distributions as an alternative class of models for multivariate count data, allowing for both overdispersion in the marginal distributions and negative correlation, while they are computationally tractable using standard ideas from finite mixture modelling.About:
This article is published in Journal of Statistical Planning and Inference.The article was published on 2007-06-01. It has received 116 citations till now. The article focuses on the topics: Count data & Multivariate statistics.read more
Citations
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Journal ArticleDOI
Flexible bivariate Poisson integer-valued GARCH model
Yan Cui,Qi Li,Fukang Zhu +2 more
TL;DR: In this paper, the authors considered a class of flexible bivariate Poisson INGARCH(1,1) model whose dependence is established by a special multiplicative factor and employed the maximization by parts algorithm and its modified version together with the alternative method by using R package Template Model Builder to estimate the parameters of interest.
Journal ArticleDOI
A finite mixture model for multivariate counts under endogenous selectivity
TL;DR: A selection model for multivariate counts, where association between the primary outcomes and the endogenous selection source is modeled through outcome-specific latent effects which are assumed to be dependent across equations, is described.
Journal ArticleDOI
Research on Load Spectrum Construction of Bench Test Based on Automotive Proving Ground
TL;DR: The rain-flow counting method is adopted to extract the load cycles from the random loads, and a statistical method based on the mixture distribution model is proposed to analyze the multimodality of load cycles.
Posted ContentDOI
A Multivariate Discrete Poisson-Lindley Distribution: Extensions and Actuarial Applications
TL;DR: In this article, the authors proposed multivariate versions of the continuous Lindley mixture of Poisson distributions considered by Sankaran (1970), which can be used for modelling multivariate dependent count data when marginal overdispersion is present.
Journal ArticleDOI
Bayesian multivariate Poisson mixtures with an unknown number of components
TL;DR: Bayesian analysis of finite mixtures of multivariate Poisson distributions with an unknown number of components is presented, and this approach is applied to a problem in multivariate disease mapping, namely joint modelling of diseases with correlated counts.
References
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BookDOI
Finite mixture models: McLachlan/finite mixture models
Geoffrey J. McLachlan,David Peel +1 more
TL;DR: The important role of finite mixture models in statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the statistical and geospatial literature.
Book
Finite Mixture Models
Geoffrey J. McLachlan,David Peel +1 more
TL;DR: The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the mathematical and statistical literature.
Journal ArticleDOI
Mixture densities, maximum likelihood, and the EM algorithm
TL;DR: This work discusses the formulation and theoretical and practical properties of the EM algorithm, a specialization to the mixture density context of a general algorithm used to approximate maximum-likelihood estimates for incomplete data problems.
Journal ArticleDOI
Model-based Gaussian and non-Gaussian clustering
TL;DR: The classification maximum likelihood approach is sufficiently general to encompass many current clustering algorithms, including those based on the sum of squares criterion and on the criterion of Friedman and Rubin (1967), but it is restricted to Gaussian distributions and it does not allow for noise.
Journal ArticleDOI
On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)
TL;DR: In this paper, a hierarchical prior model is proposed to deal with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context, which can be used as a basis for a thorough presentation of many aspects of the posterior distribution.