Arrangementer - Side 8
Michael Scheuerer will talk about (Institute of Applied Mathematics, Heidelberg University)
Statistical post-processing of weather forecasts: The importance of spatial modeling
Thomas Jaki (Department of Mathematics and Statistics, Lancaster University) will talk about
Designing multi-arm multi-stage clinical studies
Matthew Sperrin (Department of Mathematics and Statistics, Lancaster University) will talk about
Modelling the effect of interventions on onset and progression of chronic disease
Bin Yu (Departments of Statistics and Electrical Engineering & Computer Science, UC Berkeley) will talk about
Spectral clustering and high-dim stochastic block model for undirected and directed graphs
Fabio Divino (University of Molise, Italy) will talk about
MCMC computation for Bayesian modeling of presence-only data
Kukatharmini Tharmaratnam (Department of Mathematics, University of Oslo) will talk about
Monotone splines lasso
Thordis Thorarinsdottir (Norwegian Computing Center) will talk about
Proper scoring rules and divergences to evaluate weather and climate models
Alex Lenkoski (Norwegian Computing Center and Statistics for Innovation) will talk about
Hierarchical Gaussian Graphical Models: Reversible Jump and Beyond
Peder Østbye (Simonsen Advokatfirma) will talk about
Econometrical and statistical models in competition law evidence assessments
Raazesh Sainudiin (Department of Mathematics and Statistics, University of Canterbury) will talk about
Minimum Distance Estimation over Adaptive Histograms from Randomized Priority Queues on Statistical Regular Pavings
Øystein Sørensen (Department of Biostatistics, UiO) will talk about
Penalized Regression with Measurement Error
Over the last decades important methodological advances have been made for designing and analysing nested case-control and case-cohort studies. While some of these methodologies need further developments before they can be widely used, others have matured to a level that makes them ready to use in epidemiological practice. The focus of the workshop is on the methodologies that are ready to use. The workshop is aimed at biostatisticians and epidemiologists who work with population registry data and large cohorts studies. It is assumed that the participants are familiar with cohort and classical case-control studies and the regression models used to analyse them (logistic regression and Cox regression).
Rand Kwong Yew Low (UQ Business School, University of Queensland, Brisbane) will talk about
Portfolio optimization based on pair-copula constructions
Linn Cecilie Bergersen (Matematisk Institutt, Universitetet i Oslo) skal snakke om
Preselection in High-dimensional Penalized Regression Problems Guided by Freezing
Paola Vicard (Universita di Roma Tre) will talk about
Bayesian networks in official statistics
Andras Zempleni (Eötvös Loránd University Budapest) skal snakke om
Statistical modeling in moderately high dimensions with emphasis on extremes
Idris Eckley (Department of Mathematics and Statistics, Lancaster University) skal snakke om
Alias detection and spectral correction for locally stationary time series
Jonathan Tawn (Department of Mathematics and Statistics, Lancaster University) skal snakke om
Applying multivariate extreme value methods for univariate and spatial flood risk assessment
Gudmund Horn Hermansen (Matematisk Institutt, Universitetet i Oslo) skal snakke om
Model selection issues in Gaussian time series models
Peter Friis-Hansen, Det Norske Veritas, skal snakke om
Structuring complex systems using Bayesian network
Arne Bang Huseby (Matematisk Institutt, Universitetet i Oslo) skal snakke om
A framework for multi-reservoir production optimization
Nathalie Støer (Matematisk Institutt, Universitetet i Oslo) skal snakke om
Reuse of controls in nested case-control designs with application to a study of prostate cancer and vitamin D
Sjur Westgaard (NTNU) skal snakke om
Modelling and forecasting electricity price risk using volatility adjusted quantile regression
Erik Vanem (Matematisk Institutt, Universitetet i Oslo) skal snakke om
A Bayesian hierarchical space-time model of significant wave height