33 citations · 59 across the 25 of their papers we have counts for
26 papers
An explicit link between graphical models and Gaussian Markov random fields on metric graphs
David Bolin, Alexandre B. Simas, Jonas Wallin
We derive an explicit link between Gaussian Markov random fields on metric graphs and graphical models, and in particular show that a Markov random field restricted to the vertices…
Spatial confounding under infill asymptotics
David Bolin, Jonas Wallin
The estimation of regression parameters in spatially referenced data plays a crucial role across various scientific domains. A common approach involves employing an additive regres…
Weak pattern convergence for SLOPE and its robust versions
Ivan Hejný, Jonas Wallin, Małgorzata Bogdan
The Sorted L-One Estimator (SLOPE) is a popular regularization method in regression, which induces clustering of the estimated coefficients. That is, the estimator can have coeffic…
Statistical inference for Gaussian Whittle-Matérn fields on metric graphs
David Bolin, Alexandre Simas, Jonas Wallin
Whittle-Matérn fields are a recently introduced class of Gaussian processes on metric graphs, which are specified as solutions to a fractional-order stochastic differential equatio…
Markov properties of Gaussian random fields on compact metric graphs
David Bolin, Alexandre B. Simas, Jonas Wallin
There has recently been much interest in Gaussian fields on linear networks and, more generally, on compact metric graphs. One proposed strategy for defining such fields on a metri…
Coordinate Descent for SLOPE
Johan Larsson, Quentin Klopfenstein, Mathurin Massias +1
The lasso is the most famous sparse regression and feature selection method. One reason for its popularity is the speed at which the underlying optimization problem can be solved.…