dc.contributor.author | Thorén, Alexander | |
dc.date.accessioned | 2023-03-28T14:29:45Z | |
dc.date.available | 2023-03-28T14:29:45Z | |
dc.date.issued | 2023-03-28 | |
dc.identifier.uri | https://hdl.handle.net/2077/75740 | |
dc.description.abstract | Point process learning is a new statistical theory that gives us a way to estimate
parameters using cross-validation for point processes. By thinning a point
pattern we are able to create training and validation sets which are then used
in prediction errors. These errors give us a way to measure the discrepancy
between two point processes and are used to measure how well the training sets
can predict the validation sets. We investigate non-parametric intensity estimation
methods with a focus on the resample-smoothing Voronoi estimator. This
estimator works by repeatedly thinning a point pattern, finding the Voronoi
intensity estimate of the thinned point pattern, and then using the mean as the
final intensity estimate. Previously, only a thumb rule was given as to how to
choose parameters for the resample-smoothing Voronoi estimator but with the
help of point process learning we now have a data-driven method to estimate
these parameters. | en |
dc.language.iso | eng | en |
dc.title | Point process learning for non-parametric intensity estimation with focus on Voronoi estimation | en |
dc.type | text | |
dc.setspec.uppsok | PhysicsChemistryMaths | |
dc.type.uppsok | H2 | |
dc.contributor.department | University of Gothenburg/Department of Mathematical Science | eng |
dc.contributor.department | Göteborgs universitet/Institutionen för matematiska vetenskaper | swe |
dc.type.degree | Student essay | |