Events - Page 7

Peter Müller (Division of Statistics + Scientific Computation, The University of Texas at Austin) will talk about

A Nonparametric Bayesian Model for Local Clustering

Kristoffer Herland Hellton (Department of Biostatistics, UiO) will talk about

Consistency in high-dimensional principal component analysis

Benjamin Holcblat (Department of Financial Economics, BI) will talk about

A Classical Moment-Based Approach with Bayesian Properties: Econometric Theory and Empirical Evidence from Asset Pricing

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