Theory & Methods: Modelling Correlated Zero‐inflated Count Data

By Melissa J. Dobbie, Alan H. Welsh. 2001

99 citations recorded by the source corpus.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/aaf03ca6510cb1fb7451f93b8136ebabc166c011

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In Genetics

In Statistics and Probability

Citations in the corpus

Modelling skewed data with many zeros: A simple approach combining ordinary and logistic regression (2005, background): “Dobbie and Welsh (2001) extended the model to deal with serial dependence in repeated measurements. The general idea behind this approach is mentioned by Manly, McDonald and Thomas (1993, Section 10.4), in the context of modelling the amount of resource used by a population of animals. In a fisherie”
Many zeros does not mean zero inflation: comparing the goodness‐of‐fit of parametric models to multivariate abundance data (2005, unknown): “The approach of Welsh et al. (1996) has subsequently been used to fit zero-inflated distributions to abundances in a range of different contexts (Lindenmayer et al., 1999; Welsh et al., 2000; Dobbie and Welsh, 2001; Pearce and Ferrier, 2001).”
Association of predators and prey at frontal features in the California Current: competition, facilitation, and co-occurrence (2009, unknown): “To address this problem, we chose to analyze these data in 2 stages, similar theoretically to the 2-step modeling approach advocated by Dobbie & Welsh (2001) and Cunningham & Lindenmayer (2005).”
Marginal models for zero inflated clustered data (2004, methodology): “, 2002), or to incorporate generalized estimating equations (GEEs) with a dependence working correlation matrix into the fitting algorithm (Dobbie and Welsh, 2001).”
Random effect models for repeated measures of zero-inflated count data (2005, methodology): “Dobbie and Welsh (2001) applied marginal models using the generalized estimating equations approach for both parts of a hurdle model.”
Space—time zero‐inflated count models of Harbor seals (2007, methodology): “There are increasing numbers of examples where models combine these subjects, such as space–time models for Gaussian data (Wikle et al., 1998), temporal ZIP models (Dobbie and Welsh, 2001; Lee et al., 2006), and spatial ZIP models (Agarwal et al., 2002; Rathbun and Fei, 2006).”
On the Use of Zero-Inflated and Hurdle Models for Modeling Vaccine Adverse Event Count Data (2006, background): “Dobbie and Welsh (2001) and Hall and Zhang (2004) used a generalized estimating equations (GEEs) or an extension thereof to estimate hurdle and zeroinflated marginal models, respectively.”