Results 271 to 280 of about 41,785 (303)

Multiple imputation of incomplete zero‐inflated count data

Statistica Neerlandica, 2013
Empirical count data are often zero‐inflated and overdispersed. Currently, there is no software package that allows adequate imputation of these data. We present multiple‐imputation routines for these kinds of count data based on a Bayesian regression approach or alternatively based on a bootstrap approach that work as add‐ons for the popular multiple ...
Kleinke, Kristian, Reinecke, Jost
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Zero-inflated Bell regression models for count data

Journal of Applied Statistics, 2019
By starting from the one-parameter Bell distribution proposed recently in the statistic literature, we introduce the zero-inflated Bell family of distributions. Additionally, on the basis of the proposed zero-inflated distribution, a novel zero-inflated regression model is proposed, which is quite simple and may be an interesting alternative to usual ...
Artur J, Lemonte, Germán, Moreno-Arenas, Fredy, Castellares   +2 more
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A Note on Tests for Zero-Inflation in Correlated Count Data

Communications in Statistics - Simulation and Computation, 2011
Zero-inflated Poisson mixed regression models are popular approaches to analyze clustered count data with excess zeros. Prior to application of these models, it is essential to examine the necessity of the adjustment for zero outcomes. The existing literature, however, has focused only on score tests for testing the suitability of zero-inflated models ...
Liming Xiang, Guo Shou Teo
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Modeling Zero-Inflated Microbiome Data

2018
In this chapter, we introduce and illustrate how to model zero-inflated microbiome data. In Sect. 12.1, we briefly introduce modeling zero-inflated data. The remaining of this chapter is organized as follows: Sect. 12.2 introduce zero-inflated Poisson (ZIP) and negative binomial model (ZINB) and their implementations in real microbiome data. Section 12.
Yinglin Xia, Jun Sun, Ding-Geng Chen
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A score test for zero-inflation in multilevel count data

Computational Statistics & Data Analysis, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Abbas Moghimbeigi, Mohammad Reza Eshraghian, Kazem Mohammad, Brian McArdle   +3 more
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Bivariate Poisson–Poisson model of zero-inflated absenteeism data

Statistics in Medicine, 2006
Bimodal 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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Modeling zero inflated count data

2009
A natural approach to analyzing the effect of covariates on a count response variable is to use a Poisson regression model. A complication is that the counts are often more variable than can be explained by a Poisson model. This problem, referred to as overdispersion, has received a great deal of attention in recent literature and a number of ...
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Marginal models for zero inflated clustered data

Statistical Modelling, 2004
Over the last decade or so, there has been increasing interest in ‘zero inflated’ (ZI) regression models to account for ‘excess’ zeros in data. Examples include ZI poisson (ZIP), ZI binomial (ZIB), ZI negative binomial and ZI tobit models. Recently, extensions of these models to the clustered data case have begun to appear.
Hall, Daniel B., Zhang, Zhengang
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A Bayesian approach to zero-inflated data in extremes

Communications in Statistics - Theory and Methods, 2019
The generalized extreme value (GEV) distribution is known as the limiting result for the modeling of maxima blocks of size n, which is used in the modeling of extreme events.
Alexandre Henrique Quadros Gramosa, Fernando Ferraz do Nascimento, Fidel Ernesto Castro Morales   +2 more
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Adjusting for covariates in zero-inflated gamma and zero-inflated log-normal models for semicontinuous data

2018
Semicontinuous data consist of a combination of a point-mass at zero and a positive skewed distribution. This type of non-negative data distribution is found in data from many fields, but presents unique challenges for analysis. Specifically, these data cannot be analyzed using positive distributions, but distributions that are unbounded are also ...
openaire   +1 more source