Maximum Likelihood

Smoothing the math of spatial networks

2 min read ·
A new statistical approach could make modelling spatial networks more accurate and efficient.

Spatial Self-Confounding: Smoothness-related estimation bias in spatial regression models

1 min read ·
Spatial regression models are widely used to capture the relationship between observations and covariates, employing Gaussian random fields to account for spatial variability not explained by the covariates. A new study by researchers David Bolin and Jonas Wallin addresses a critical yet often overlooked problem in these models: smoothness-related spatial self-confounding. The work examines how misspecified covariates, particularly when there are differences in smoothness between variables, can lead to severe and counter-intuitive biases in the estimation of regression parameters. These

Turning your desktop to a Supercomputer with ExaGeoStatR

1 min read ·
Turning your desktop to a Supercomputer with ExaGeoStatR Download at https://github.com/ecrc/exageostatR. Remember the old times where you had to leave your R simulations running for the whole night on your desktop due to a large climate/weather dataset? ExaGeoStatR combines the user productivity of R with high performance computing linear algebra software libraries to deliver supercomputing-style environment right below your desk. ExaGeoStatR enables computing the maximum likelihood using large environmental datasets on R, while extracting performance from the underlying hardware resources