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Generalized additive modelling and zero inflated count data [PDF]
Ecological Modelling, 2002 This paper describes a flexible method for modelling zero inflated count data which are typically found when trying to model and predict species distributions.
Simon C Barry, A H Welsh
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On Baseline Conditions for Zero-Inflated Longitudinal Count Data [PDF]
Communications in Statistics - Simulation and Computation, 2013 We describe a mixed-effect hurdle model for zero-inflated longitudinal count data, where a baseline variable is included in the model specification. Association between the count data process and the endogenous baseline variable is modeled through a latent structure, assumed to be dependent across equations.
Antonello Maruotti, Valentina Raponi
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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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Semiparametric Analysis of Zero‐Inflated Count Data
Biometrics, 2006SummaryMedical and public health research often involve the analysis of count data that exhibit a substantially large proportion of zeros, such as the number of heart attacks and the number of days of missed primary activities in a given period. A zero‐inflated Poisson regression model, which hypothesizes a two‐point heterogeneity in the population ...
Lam, KF, Bun Cheung, Y, Xue, H
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Small Area Estimation for Zero-Inflated Data
Communications in Statistics - Simulation and Computation, 2011The commonly used method of small area estimation (SAE) under a linear mixed model may not be efficient if data contain substantial proportion of zeros than would be expected under standard model assumptions (hereafter zero-inflated data). The authors discuss the SAE for zero-inflated data under a two-part random effects model that account for excess ...
Hukum Chandra, U. C. Sud
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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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Journal of Biopharmaceutical Statistics, 2020
In longitudinal studies measurements are often collected on different types of responses for each individual. These may contain several longitudinally measured responses (such as the CD4 count) and the time at which an event occurs (e.g., HIV, death, or dropout from the study). These outcomes are often separately analyzed. Compared to separate modeling,
Mojtaba, Zeinali Najafabadi +2 more
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In longitudinal studies measurements are often collected on different types of responses for each individual. These may contain several longitudinally measured responses (such as the CD4 count) and the time at which an event occurs (e.g., HIV, death, or dropout from the study). These outcomes are often separately analyzed. Compared to separate modeling,
Mojtaba, Zeinali Najafabadi +2 more
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Sensitivity of score tests for zero‐inflation in count data
Statistics in Medicine, 2004AbstractIn many biomedical applications, count data have a large proportion of zeros and the zero‐inflated Poisson regression (ZIP) model may be appropriate. A popular score test for zero‐inflation, comparing the ZIP model to a standard Poisson regression model, was given by van den Broek.
Xiang, L, Fung, WK, Lee, AH
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On modeling zero-inflated insurance data
The Journal of Risk Model Validation, 20160 ...
Pérez Sánchez, J. M. +1 more
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A flexible zero-inflated model to address data dispersion
Computational Statistics & Data Analysis, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kimberly F. Sellers, Andrew M. Raim
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