Hierarchical Modeling and Analysis for Spatial Data

By Sudipto Banerjee, Bradley P. Carlin, Alan E. Gelfand. 2003

3,481 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a0afa4ec10b2b060aff0ec676a661c8013c7df1d

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Citations in the corpus

A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling (2010, methodology): “Much of the early work in spatial hierarchical modeling was done in the context of epidemiology; the book by Banerjee et al. (2004) gives an excellent overview of the general practice and many of the examples within reflect this origin.”
Estimation and prediction of the spatial occurrence of fish species using Bayesian latent Gaussian models (2013, methodology): “This is commonly known as Bayesian kriging (Banerjee et al 2004).”
Spatial dynamic factor analysis (2008, methodology): “…j ∼ N (m µ , S µ ), φ j ∼ IG(2, b) and τ 2 j ∼ IG(n τ /2, n τ s τ /2), j = 1, . . . , m, where m µ , S µ , n τ and s τ are known hyperparameters, b = ρ 0 /(−2 ln(0.05)) and ρ 0 = max i,j=1,...,N |s i −s j | (see Banerjee, Carlin, and Gelfand (2004) and Schmidt and Gelfand (2003), for more details).”
A MULTIVARIATE SEMIPARAMETRIC BAYESIAN SPATIAL MODELING FRAMEWORK FOR HURRICANE SURFACE WIND FIELDS (2007, methodology): “Variations of this linear model of coregionization (LMC) have been used by Grzebyk and Wackernagl (1994), Wackernagel (2003), Schmidt and Gelfand (2003), Banerjee et al. (2004), and Gelfand et al.”
Nonstationary multivariate process modeling through spatially varying coregionalization (2004, methodology): “and offered as a mulitvariate process modelling strategy in Schmidt and Gelfand (2003) and in Banerjee et al. (2004). The most basic coregionalization model, the so-called intrinsic specification dates at least to Matheron (1982).”
An intuitive Bayesian spatial model for disease mapping that accounts for scaling (2016, methodology): “We refer to Lawson [1] and Banerjee et al. [18] for a detailed description of the class of Bayesian hierarchical models.”
Multivariate Bayesian analysis of atmosphere–ocean general circulation models (2007, methodology): “This decomposition is consistent with the more traditional decomposition in spatial modeling (Cressie 1993; Banerjee et al. 2004 ) where the mean corresponds to global (first-order) behavior and the error captures”
Geostatistics of Dependent and Asymptotically Independent Extremes (2013, background): “Classical geostatistics is a well-developed field surveyed in numerous textbooks (Cressie 1993; Wackernagel 2003; Banerjee et al. 2004; Diggle and Ribeiro 2007; Cressie and Wikle 2011), with much software available and a wide range of user communities corresponding to its many applications.”
Small area estimation for spatial correlation in watershed erosion assessment (2006, background): “the spatial autoregressive coefficient, W is the spatial weight matrix for θ, I is an m × m identity matrix and u ∼ N (0 ,σ 2 uI) .I fW is defined in row standardized form, it is row stochastic, ρ ∈ (−1, 1), and it is called the spatial autocorrelation parameter ( Banerjee, Carlin, and Gelfand 2004,”
Spatial models with explanatory variables in the dependence structure (2014, background): “1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 are to explore the data, make predictions at locations where the process is unobserved, learn about the process or”
Statistical Modelling of Spatial Extremes (2012, methodology): “As suggested by Banerjee et al. (2004), informative priors should be used for the parameters α and λ of the covariance functions, in order to yield non-degenerate marginal posterior distributions for them.”
An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach (2011, background): “…equation approach Citation for published version: Lindgren, F, Rue, H & Lindström, J 2011, 'An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach' Journal of the Royal Statistical Society: Series B, vol 73, no. 4,…”
High dimensional structured additive regression models: Bayesian regularization, smoothing and predictive performance (2011, methodology): “Other options for modelling spatial effects like proper Gaussian conditional auto-regressive models and stationary Gaussian random-field (kriging) models are proper, but with the same general form; see Rue and Held (2005) or Banerjee et al. (2003). In all cases, we assume inverse gamma priors δ2 l ∼”
Spatial Regression Modeling for Compositional Data With Many Zeros (2013, methodology): “In this regard, with areal dependence in our model, internal validation for choosing γ is all that is available, since prediction with CAR models is incoherent (see Banerjee et al., 2004, Ch. 5).”