Det 21. norske statistikermøtet 

Tønsberg, 17-20 Juni, 2024                      Påmelding  

Innsending av abstrakt

Etter en lengre pause enn ønsket er vi glade for å endelig kunne invitere dere alle tilbake til det 21. norske statistikermøte i 2024! Vi ser frem til å samle det norske statistikkmiljøet igjen for å dele kunnskap og styrke felleskapet. Deres bidrag og deltagelse vil være avgjørende for å gjøre årets konferanse til en suksess.

 

Vel møtt!

Thordis L. Thorarinsdottir

Invitert foredrag

UiO

TBA


John Leonard Paige

Invitert foredrag

NTNU

Spatial Importance Weighted Cross Validation

It is well recognized that classical cross validation (CV) methods such as leave one out CV (LOOCV) are often overly optimistic when applied to geostatistical models, underestimating their predictive error, yet there is still no consensus as to which CV method should be used and when. Moreover, many recent proposals for CV methods focus on reducing or accounting for spatial correlation between the left out observations and the in-sample data used to make predictions rather than on estimating the frequent target of interest: the spatial average of prediction error within the spatial domain. In this talk, I propose a method for CV in a geostatistical interpolation context I call spatial importance weighted CV, where importance weights are used within a CV framework to produce an estimator for the spatial average of prediction error (mean squared error or another scoring metric or rule) within the spatial domain that reduces bias compared to LOOCV. Importance weights are estimated via a Voronoi estimator requiring no tuning parameters, leading to a variation on spatial block CV estimators.



Olav Nikolai Risdal Breivik

Forkurs

NR

Introduction to Template Model Builder

Template Model Builder (TMB) is a general tool for efficiently setting up highly parameterized models, also including random effects. TMB provides derivatives of the implemented function, enabling fast inference. Additionally, Markov structures are utilized to quickly marginalize over latent effects using the Laplace approximation.

At the course we will introduce TMB and focus on

1. Latent effect models

2. Spatial modeling

3. Non-standard models 

4. Model validation in TMB

During this course we will use the RTMB package, and the only programming language we will use is R.


Om møtet


Programkomité

Arrangementskomité

Norsk statistisk forening avdeling Oslo

Har du spørsmål om møtet, kontakt leder av NSF avd. Oslo, Aliaksandr Hubin på e-post: Aliaksandr.Hubin [alfakrøll] nmbu.no