Zero-inflated models with application to spatial count data

By D. K. Agarwal, Alan E. Gelfand, S. Citron-Pousty. Environmental and Ecological Statistics, 2002

286 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b8a531adeba6cec7a52c8be0b38edf91e0d7912b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bas Kempen
2. Gerard B. M. Heuvelink
3. Jay M. Ver Hoef
4. S. Bates
5. Claudia Czado
6. Tomislav Hengl
7. J. K. Jansen
8. Aaron M. Christ

In Ecology

1. J. Andrew Royle
2. Gerard B. M. Heuvelink
3. D. Dawson
4. Tomislav Hengl
5. Bas Kempen
6. S. Bates
7. Jay M. Ver Hoef
8. Claudia Czado

In Statistics and Probability

1. J. Andrew Royle
2. D. Dawson
3. Gerard B. M. Heuvelink
4. Jay M. Ver Hoef
5. S. Bates
6. Claudia Czado
7. Tomislav Hengl
8. J. K. Jansen

Citations in the corpus

• Modelling count data with overdispersion and spatial effects (2008, methodology): “Agarwal et al. (2002) use a zero inflated Poisson regression model for spatial count data in a Bayesian framework.”
• A spatial zero-inflated poisson regression model for oak regeneration (2006, methodology): “Agarwal et al. (2002) proposes a zero-inflated model for spatial count data.”
• Mixed Effects Models and Extensions in Ecology with R (2009, methodology): “, 1996; Agarwal et al., 2002; Barry and Welsh, 2002; Kuhnert et al., 2005; Minamia et al., 2007; and Potts and Elith, 2006 among several others). There are also many applications outside ecology; see, for example, Lambert (1992), Ridout et al. (1998), Xie et al. (2001), and Carrivick et al. (2003) a”
• Space—time zero‐inflated count models of Harbor seals (2007, background): “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).”
• MODELING ABUNDANCE EFFECTS IN DISTANCE SAMPLING (2004, methodology): “Under the Poisson model, the conventional deviance statistic for Poisson data (Agresti 2002) may be used in sufficiently large samples.”
• SoilGrids1km — Global Soil Information Based on Automated Mapping (2014, methodology): “N Multiple linear regression [34] (for predicting pH, sand, silt and clay percentages and bulk density), N General Linear Models (GLM’s) with log-link function [35,36] (for predicting organic carbon content and CEC), N Zero-inflated models [37] (for predicting coarse fragments and depth to bedrock; ”