67 citations · 117 across the 9 of their papers we have counts for
9 papers
A sub-hourly spatio-temporal statistical model for solar irradiance in Ireland using open-source data
Maeve Upton, Eamonn Organ, Amanda Lenzi +1
Accurate estimation of solar irradiance is essential for reliable modelling of solar photovoltaic (PV) power production. In Ireland's highly variable maritime climate, where ground…
Automatic cross-validation in structured models: Is it time to leave out leave-one-out?
A. Adin, E. Krainski, A. Lenzi +3
Standard techniques such as leave-one-out cross-validation (LOOCV) might not be suitable for evaluating the predictive performance of models incorporating structured random effects…
Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods
Julia Walchessen, Amanda Lenzi, Mikael Kuusela
In spatial statistics, fast and accurate parameter estimation, coupled with a reliable means of uncertainty quantification, can be challenging when fitting a spatial process to rea…
Towards black-box parameter estimation
Amanda Lenzi, Haavard Rue
Deep learning algorithms have recently shown to be a successful tool in estimating parameters of statistical models for which simulation is easy, but likelihood computation is chal…
Spatio-Temporal Cross-Covariance Functions under the Lagrangian Framework with Multiple Advections
Mary Lai O. Salvaña, Amanda Lenzi, Marc G. Genton
When analyzing the spatio-temporal dependence in most environmental and earth sciences variables such as pollutant concentrations at different levels of the atmosphere, a special p…
Neural Networks for Parameter Estimation in Intractable Models
Amanda Lenzi, Julie Bessac, Johann Rudi +1
We propose to use deep learning to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible. We show how to estimate par…
Parameter Estimation with Dense and Convolutional Neural Networks Applied to the FitzHugh-Nagumo ODE
Johann Rudi, Julie Bessac, Amanda Lenzi
Machine learning algorithms have been successfully used to approximate nonlinear maps under weak assumptions on the structure and properties of the maps. We present deep neural net…
Improving Bayesian Local Spatial Models in Large Data Sets
Amanda Lenzi, Stefano Castruccio, Haavard Rue +1
Environmental processes resolved at a sufficiently small scale in space and time will inevitably display non-stationary behavior. Such processes are both challenging to model and c…
Analysis of Aggregated Functional Data from Mixed Populations with Application to Energy Consumption
Amanda Lenzi, Camila P. E. de Souza, Ronaldo Dias +2
Understanding the energy consumption patterns of different types of consumers is essential in any planning of energy distribution. However, obtaining consumption information for si…