04661nam a22006255i 4500 978-3-031-65723-8 DE-He213 20251030112625.0 cr nn 008mamaa 240712s2024 sz | s |||| 0|eng d 9783031657238 978-3-031-65723-8 10.1007/978-3-031-65723-8 doi QA276-280 PBT bicssc MAT029000 bisacsh PBT thema Developments in Statistical Modelling [electronic resource] / edited by Jochen Einbeck, Hyeyoung Maeng, Emmanuel Ogundimu, Konstantinos Perrakis. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. X, 270 p. 94 illus., 78 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Contributions to Statistics, 2628-8966 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. 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. Statistics. 65982 Regression analysis. 61412 Statistics . 65982 Machine learning. 55059 Biometry. 65981 Statistical Theory and Methods. Linear Models and Regression. 76680 Bayesian Inference. 76436 Statistical Learning. 67991 Biostatistics. 65985 Einbeck, Jochen. editor. edt http://id.loc.gov/vocabulary/relators/edt 80437 Maeng, Hyeyoung. editor. edt http://id.loc.gov/vocabulary/relators/edt 80438 Ogundimu, Emmanuel. editor. edt http://id.loc.gov/vocabulary/relators/edt 80439 Perrakis, Konstantinos. editor. edt http://id.loc.gov/vocabulary/relators/edt 80440 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031657221 Printed edition: 9783031657245 Printed edition: 9783031657252 Contributions to Statistics, 2628-8966 80441 https://doi.org/10.1007/978-3-031-65723-8 ZDB-2-SMA ZDB-2-SXMS 61538 61538