LEADER 04405nam 22007335 450 001 9910874655003321 005 20250320145225.0 010 $a9783031657238 010 $a3031657233 024 7 $a10.1007/978-3-031-65723-8 035 $a(MiAaPQ)EBC31526150 035 $a(Au-PeEL)EBL31526150 035 $a(CKB)32742178400041 035 $a(DE-He213)978-3-031-65723-8 035 $a(OCoLC)1446436286 035 $a(EXLCZ)9932742178400041 100 $a20240712d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDevelopments in Statistical Modelling /$fedited by Jochen Einbeck, Hyeyoung Maeng, Emmanuel Ogundimu, Konstantinos Perrakis 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (281 pages) 225 1 $aContributions to Statistics,$x2628-8966 311 08$a9783031657221 311 08$a3031657225 327 $aREML 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. 330 $aThis volume on the latest developments in statistical modelling is a collection of refereed papers presented at the 38th International Workshop on Statistical Modelling, IWSM 2024, held from 14 to 19 July 2024 in Durham, UK. The contributions cover a wide range of topics in statistical modelling, including generalized linear models, mixture models, regularization techniques, hidden Markov models, smoothing methods, censoring and imputation techniques, Gaussian processes, spatial statistics, shape modelling, goodness-of-fit problems, and network analysis. Various highly topical applications are presented as well, especially from biostatistics. The approaches are equally frequentist and Bayesian, a categorization the statistical modelling community has synergetically overcome. The book also features the workshop?s keynote contribution on statistical modelling for big and little data, highlighting that both small and large data sets come with their own challenges. The International Workshop on Statistical Modelling (IWSM) is the annual workshop of the Statistical Modelling Society, with the purpose of promoting important developments, extensions, and applications in statistical modelling, and bringing together statisticians working on related problems from various disciplines. This volume reflects this spirit and contributes to initiating and sustaining discussions about problems in statistical modelling and triggers new developments and ideas in the field. 410 0$aContributions to Statistics,$x2628-8966 606 $aStatistics 606 $aRegression analysis 606 $aStatistics 606 $aMachine learning 606 $aBiometry 606 $aStatistical Theory and Methods 606 $aLinear Models and Regression 606 $aBayesian Inference 606 $aStatistical Learning 606 $aBiostatistics 615 0$aStatistics. 615 0$aRegression analysis. 615 0$aStatistics. 615 0$aMachine learning. 615 0$aBiometry. 615 14$aStatistical Theory and Methods. 615 24$aLinear Models and Regression. 615 24$aBayesian Inference. 615 24$aStatistical Learning. 615 24$aBiostatistics. 676 $a519.5 700 $aEinbeck$b Jochen$01749638 701 $aMaeng$b Hyeyoung$01749639 701 $aOgundimu$b Emmanuel$01749640 701 $aPerrakis$b Konstantinos$01749641 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910874655003321 996 $aDevelopments in Statistical Modelling$94183936 997 $aUNINA