Developments in Statistical Modelling (eBook, PDF)

Redaktion: Einbeck, Jochen; Perrakis, Konstantinos; Ogundimu, Emmanuel; Maeng, Hyeyoung

• Format: PDF

Produktbeschreibung

This volume on the latest developments in statistical modelling is a collection of refereed papers presented at the 38th International Workshop on Statistical Modelling, IWSM 2024, held from 14 to 19 July 2024 in Durham, UK. The contributions cover a wide range of topics in statistical modelling, including generalized linear models, mixture models, regularization techniques, hidden Markov models, smoothing methods, censoring and imputation techniques, Gaussian processes, spatial statistics, shape modelling, goodness-of-fit problems, and network analysis. Various highly topical applications are presented as well, especially from biostatistics. The approaches are equally frequentist and Bayesian, a categorization the statistical modelling community has synergetically overcome. The book also features the workshop's keynote contribution on statistical modelling for big and little data, highlighting that both small and large data sets come with their own challenges.

The International Workshop on Statistical Modelling (IWSM) is the annual workshop of the Statistical Modelling Society, with the purpose of promoting important developments, extensions, and applications in statistical modelling, and bringing together statisticians working on related problems from various disciplines. This volume reflects this spirit and contributes to initiating and sustaining discussions about problems in statistical modelling and triggers new developments and ideas in the field.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 270
• Erscheinungstermin: 11. Juli 2024
• Englisch
• ISBN-13: 9783031657238
• Artikelnr.: 71254266

Autorenporträt

Jochen Einbeck is Professor of Statistics at Durham University, UK, Co-Director of the Durham Research Methods Centre, and Chair of IWSM 2024. He has been a regular attendee of the IWSM conferences since co-hosting the workshop in 2006 in Galway. He is Associate Editor of Statistical Modelling and Advances in Statistical Analysis, having previously served as Associate Editor of CSDA and Statistics. His research interests include mixture models, nonparametric regression, principal curves and random effect modelling. Hyeyoung Maeng is an Assistant Professor of Statistics at Durham University, UK. Her research interests include change-point and feature detection, high-dimensional statistics, data-adaptive and multiscale methods and factor analysis. She is also interested in applying statistical methods in economics, finance and environment. Emmanuel Ogundimu is an Associate Professor of Statistics at Durham University, UK and Co-Director of the Durham Biostatistics Unit. He leads a team of researchers on statistical aspects of clinical trials. His scholarly work centers on applying and developing statistical methodology and learning algorithms. Konstantinos Perrakis is an Assistant Professor of Statistics at Durham University, UK. His research focuses on aspects of Bayesian and computational statistics and on statistical models for high-dimensional problems. He has applied his research to the fields of biomedicine, epidemiology and transportation.

Inhaltsangabe

REML for two dimensional P splines.- Learning Bayesian networks from ordinal data The Bayesian way.- Latent Dirichlet allocation and hidden Markov models to identify public perception of sustainability in social media data.- Bayesian approaches to model overdispersion in Spatio temporal binomial data.- Elicitation of priors for intervention effects in educational trial data.- Elicitation of priors for intervention effects in educational trial data.- Optimism correction of the AUC with complex survey data.- Statistical models for patient centered outcomes in clinical studies.- Bayesian hidden Markov models for early warning.- A Bayesian Markov-switching for smooth modelling of extreme value distributions.