7 citations · 13 across the 5 of their papers we have counts for
6 papers
The partial K function
Jake P. Grainger, Tuomas A. Rajala, David J. Murrell +1
The K function and its related statistics have been an enduring tool in the analysis of spatial point processes, providing an easy to compute and interpret summary statistic for ch…
Spectral estimation for spatial point processes and random fields
Jake P. Grainger, Tuomas A. Rajala, David J. Murrell +1
Spatial variables can be observed in many different forms, such as regularly sampled random fields (lattice data), point processes, and randomly sampled spatial processes. Joint an…
Visualizing the Wavenumber Content of a Point Pattern
Jake P. Grainger, Tuomas A. Rajala, David J. Murrell +1
Spatial point patterns are a commonly recorded form of data in ecology, medicine, astronomy, criminology, epidemiology and many other application fields. One way to understand thei…
A multivariate pseudo-likelihood approach to estimating directional ocean wave models
Jake P. Grainger, Adam M. Sykulski, Kevin Ewans +2
Ocean buoy data in the form of high frequency multivariate time series are routinely recorded at many locations in the world's oceans. Such data can be used to characterise the oce…
What is the Fourier Transform of a Spatial Point Process?
Tuomas A. Rajala, Sofia C. Olhede, Jake P. Grainger +1
This paper determines how to define a discretely implemented Fourier transform when analysing an observed spatial point process. To develop this transform we answer four questions;…
Estimating the parameters of ocean wave spectra
Jake P. Grainger, Adam M. Sykulski, Philip Jonathan +1
Wind-generated waves are often treated as stochastic processes. There is particular interest in their spectral density functions, which are often expressed in some parametric form.…