5 papers · 1 filter
A Unified and Computationally Efficient Non-Gaussian Statistical Modeling Framework
David Bolin, Xiaotian Jin, Alexandre B. Simas +1
Datasets that exhibit non-Gaussian characteristics are common in many fields, while the current modeling framework and available software for non-Gaussian models is limited. We int…
Log-Gaussian Cox Processes on General Metric Graphs
David Bolin, Damilya Saduakhas, Alexandre B. Simas
The modeling of spatial point processes has advanced considerably, yet extending these models to non-Euclidean domains, such as road networks, remains a challenging problem. We pro…
A new class of non-stationary Gaussian fields with general smoothness on metric graphs
David Bolin, Lenin Riera-Segura, Alexandre B. Simas
The increasing availability of network data has driven the development of advanced statistical models specifically designed for metric graphs, where Gaussian processes play a pivot…
Fast and robust cross-validation-based scoring rule inference for spatial statistics
Helga Kristin Olafsdottir, Holger Rootzén, David Bolin
Scoring rules are aimed at evaluation of the quality of predictions, but can also be used for estimation of parameters in statistical models. We propose estimating parameters of mu…
Wasserstein complexity penalization priors: a new class of penalizing complexity priors
David Bolin, Alexandre B. Simas, Zhen Xiong
Penalizing complexity (PC) priors provide a principled framework for reducing model complexity by penalizing the Kullback--Leibler Divergence (KLD) between a ``simple'' base model…