Statistical Modelling Society

37th International Workshop on Statistical Modelling, Dortmund, Germany, 2023, Proceedings

Proceedings of the 37th International Workshop on Statistical Modelling: July 17-21, 2023 - Dortmund, Germany.

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Contents

Part I: Invited papers

Gerharz and Kolodziej

Data science meets football

Iannario

Robust regression modelling for ordinal categorical data

Heller

Back to the future: model what you measure

Majumder et al.

Modeling extremal streamflow using deep learning approximations and a flexible spatial process

Wood

On Covid, dynamic models and inferring smooth functions

Part II: Contributed papers

Adam et al.

State-switching decision trees

Alfonzeti et al.

Efficient stochastic learning of graphical structures for large-scale mixed data surveys

Arce Guillen et al.

Flexible habitat selection analysis with generalized additive models

Balestra et al.

An information-theoretic perspective on double descent in flooded boosting

Berger and Staerk

Adaptive random forests for high-dimensional regression

Brusa et al.

Evolutionary algorithm for the estimation of discrete latent variables models

Carmada and Durbán

Coherent cause-specific mortality forecasting via constrained penalized regression models

Claes et al.

The influence of resolution on the predictive power of spatial heterogeneity measures as a biomarker of disease severity

Cuevas Andrade et al.

A multi-state model for the natural history of prostate cancer; using data from a screening trial

de Cavalho and Lee

Bayesian smoothing for joint extremes

Di Maria and Muggeo

Semi-parametric estimation of growth curves

Feldmann et al.

Modelling time-of-day variation in hidden Markov models using cyclic P-splines

Ge et al.

Bayesian inference of dynamic models emulated with a time series Gaussian process

Gioia et al.

Gradient boosting for parsimonious additive covariance matrix modelling

Golovkine et al.

Functional multilevel modelling of the influence of the menstrual cycle on the performance of female cyclists

Griesbach and Hepp

Confidence intervals for finite mixture regression based on resampling techniques

Hepp et al.

Component-wise boosting for mixture distributional regression models

Hoshiyar and Gertheiss

Fusion, smoothing and model selection for item-on-item regression

Inácio and Rodríguez-Álvarez.

Induced nonparametric ROC surface regression

Janssens et al.

Assessing spatial trends in health outcomes using primary care registry data

Kaufmann and Kateri

Statistical inference for high-dimensional logistic regression: Variable selection and levels fusion for categorical covariates

Klinkhammer et al.

Advanced statistical modelling for polygenic risk scores based on large cohort data

Kolb et al.

Sparse modality regression

Lambardi di San Miniato et al.

On prediction via equal-tailed intervals with an application to sensor data analytics

Lambert and Gressani

Asymmetry issues with non-penalized parameters in Laplace P-splines models

Laverny and Lambert

Local moment matching with Gamma mixtures and automatic smoothness selection

Limpoco et al.

Linear mixed modelling of federated data when only the mean, covariance, and sample size are available

McInerney and Burke

Feedforward neural networks from a statistical-modelling perspective

Mews et al.

Modelling medical claims data using Markov-modulated marked Poisson processes

Millán et al.

Estimating what is under the tip of gender-based violence: A statistical modelling approach

Mlynarczyk et al.

A bivariate Poisson regression model for radiation dose estimation

Morales-Otero and Núñez-Antón

Bayesian spatio-temporal conditional overdispersion models proposals

Ötting and Langrock

Lasso-based order selection in hidden Markov models: a case study using stock market data

Orsini et al.

Bayesian survival analysis using pseudo-observations

Page et al.

Clustering anterior cruciate ligament reconstruction patients using functional walking biomechanics

Palma et al.

Forecasting insect abundance using time series embedding and environmental covariates

Pohle et al.

Studying animal interactions with Markov-switching step-selection models

Potts et al.

Prediction-based variable selection for component-wise gradient boosting

Radvanyi et al.

Computationally efficient ranking of groundwater monitoring locations

Riebl et al.

A distributional regression approach for Gaussian process responses

Rodrigues de Lara et al.

Multi-state models for double transitions associated with parasitism in biological control

Sterzinger and Kosmidis

Bias reduced predictions for black-box models

Stoye and Langrock

Autoregressive hidden Markov models for high-resolution animal movement data

Strömer et al.

Complexity reduction via deselection for boosting distributional copula regression

Sumalinab et al.

Bayesian nowcasting with Laplacian-P-splines

Umlauf et al.

Boosting distributional soft regression trees

Urdangarin et al.

A one-step spatial+ approach to mitigate spatial confounding in multivariate spatial areal models

Wang et al.

Extending central statistical monitoring to electronic patient-reported outcomes in clinical trials

Weiß

Ordinal compositional data and time series

Wetscher et al.

Stagewise boosting distributional regression

Wilson et al.

Gaussian process models: From astrophysics to industrial data

Zhang et al.

A multilevel multivariate response model for data with latent structures

Zumeta-Olaskoaga et al.

Flexible modelling of time-varying training exposures on the risk of recurrent injuries in football

Part III : Contributed papers

66 further articles

Please open Proceedings Volume for Table of Contents.