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Zero-Inflated Poisson Model in Statistical Process Control
Computational Statistics and Data Analysis, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
T N Goh
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Bivariate Poisson–Poisson model of zero-inflated absenteeism data
Statistics in Medicine, 2006Bimodal distributions of counts with one mode at zero are often seen in medical research. In a health survey parents were asked the number of days their children missed their activities (Y(1)) and the number of days their children spent in bed (Y(2)) due to illness in the past four weeks. Both variables exhibited zero inflation. We consider a bivariate
Lam, KF, Cheung, YB
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The analysis of zero‐inflated count data: Beyond zero‐inflated Poisson regression.
British Journal of Mathematical and Statistical Psychology, 2011Infrequent count data in psychological research are commonly modelled using zero‐inflated Poisson regression. This model can be viewed as a latent mixture of an “always‐zero” component and a Poisson component. Hurdle models are an alternative class of two‐component models that are seldom used in psychological research, but clearly separate the zero ...
Loeys, Tom +3 more
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Document analysis and visualization with zero-inflated poisson
Data Mining and Knowledge Discovery, 2009Data visualization is aimed at obtaining a graphic representation of high dimensional information. A data projection over a lower dimensional space is pursued, looking for some structure on the projections. Among the several data projection based methods available, the Generative Topographic Mapping (GTM) has become an important probabilistic framework
Dora Alvarez, Hugo Hidalgo 0001
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Score Tests for Zero-Inflated Poisson Models
Computational Statistics & Data Analysis, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jansakul, N., Hinde, J. P.
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A Bayesian Analysis of Zero-Inflated Generalized Poisson Model
Computational Statistics and Data Analysis, 2003In several real-life examples one encounters count data where the number of zeros is such that the usual Poisson distribution does not fit the data. Quite often the number of zeros is large, and hence the data is zero inflated. In this situation, a zero-inflated generalized Poisson model can be considered and a Bayesian analysis can be carried out ...
Atanu Biswas, Jean-François Angers
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Zero-Inflated Sum of Conway-Maxwell-Poissons (ZISCMP) Regression
Journal of Statistical Computation and Simulation, 2019 While excess zeros are often thought to cause data over-dispersion (i.e. when the variance exceeds the mean), this implication is not absolute. One should instead consider a flexible class of distributions that can address data dispersion along with ...
Kimberly Sellers, Derek S Young
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Zero-Inflated Poisson Regression for Longitudinal Data
Communications in Statistics - Simulation and Computation, 2009Medical and public health research often involve the analysis of repeated or longitudinal count data that exhibit excess zeros such as the number of yearly doctor visits by a group of individuals over a number of years. Zero-inflated Poisson (ZIP) regression models can be used to account for excess zeros in count data.
M. Tariqul Hasan, Gary Sneddon
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ZERO-INFLATED POISSON REGRESSION MODELS: APPLICATIONS IN THE SCIENCES AND SOCIAL SCIENCES
Annals of Financial Economics, 2021This paper makes a theoretical contribution by presenting a detailed derivation of a zero-inflated Poisson (ZIP) model, and then deriving the parameters of the ZIP model using a fishing data set. This model has several practical applications, and is largely performed to model count data that have an excess number of zero counts.
Truong, Buu Chau +3 more
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A Zero-inflated Poisson Model of Migration Frequency
International Regional Science Review, 1996This paper shows that the frequency of migration can be best modeled by a zero-inflated Poisson process, because it takes into account the overwhelming presence of zeros (nonmigrants) in the data. A failure to do so can cause the coefficients to be biased and also result in poor prediction.
A K, Bohara, R G, Krieg
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