Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
By Peter J. Diggle. 2013
719 citations recorded by the source corpus.
Subfields: Building and Construction, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/00354079656b86cd6e496ff8b109bd4006dab156
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Building and Construction
- Flávio B. Gonçalves
- P. Grabarnik
- Dani Gamerman
- Vipin Kumar
- Mari Myllymäki
- Anuj Karpatne
- Mevin B. Hooten
- Tomás Mrkvicka
In Environmental Engineering
- Vipin Kumar
- Anuj Karpatne
- Flávio B. Gonçalves
- P. Grabarnik
- Dani Gamerman
- Mari Myllymäki
- Mevin B. Hooten
- Tomás Mrkvicka
Citations in the corpus
- Hierarchical Species Distribution Models (2016, background): “A statistical model describing the pattern created by points in space and time is the space-time Poisson point process distribution [16, 20].”
- How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions (2019, methodology): “Traditionally, geostatistical regression methods have been extensively used for space infilling based on point measurements, but most methods assume Gaussian-like data distributions [174, 175].”
- Spatio-Temporal Data Mining (2017, methodology): “Existing research in STDM includes foundational research in the statistics community [Cressie and Wikle 2015], e.g., research on spatio-temporal point processes [Diggle 2013].”
- Exact Bayesian inference in spatiotemporal Cox processes driven by multivariate Gaussian processes (2015, methodology): “The main ones are reviewed in Gamerman et al. (2007) and include independent GP’s, dependent processes with a common correlation function, or linear mixtures of independent GP’s. Extensions to cope with space and time were introduced by Gelfand et al. (2005). A process β follows a dynamic Gaussian p”
- Global envelope tests for spatial processes (2013, methodology): “The exchangeability assumption is useful because it allows us to use Barnard’s Monte Carlo test, e.g. when the test statistic is a deviation measure based on an unknown theoretical expectation (see for example Diggle (2013) and Davison and Hinkley (1997)).”