Hierarchical statistical modeling of big spatial datasets using the exponential family of distributions

By Aritra Sengupta, Noel Cressie. 2013

54 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/647bc1d9d87da59268b73afaabade52eda9a1903

How researchers see this paper

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

In Economics and Econometrics

1. A. Zammit‐Mangion
2. Matthew Sainsbury-Dale
3. Noel Cressie

In Environmental Engineering

1. A. Zammit‐Mangion
2. Matthew Sainsbury-Dale
3. Noel Cressie
4. Aritra Sengupta
5. Emily Lei Kang
6. Finn Lindgren
7. Francky Fouedjio

In Statistics and Probability

1. A. Zammit‐Mangion
2. Matthew Sainsbury-Dale
3. Noel Cressie
4. Finn Lindgren
5. Francky Fouedjio

Citations in the corpus

• Basis-Function Models in Spatial Statistics (2021, methodology): “In a remote-sensing, ‘big data’ application, Sengupta & Cressie (2013) showed the computational advantages of using the spatial GLMM framework; Equation 17 was modelled with a gamma distribution, g(·) = log(·), and g(Y (·)) in Equation 18 was given by the basis-function model, Equation 6.”