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024 7 _a10.1007/978-3-031-65723-8
_2doi
050 4 _aQA276-280
072 7 _aPBT
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072 7 _aMAT029000
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072 7 _aPBT
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245 1 0 _aDevelopments in Statistical Modelling
_h[electronic resource] /
_cedited by Jochen Einbeck, Hyeyoung Maeng, Emmanuel Ogundimu, Konstantinos Perrakis.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aX, 270 p. 94 illus., 78 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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_2rdacarrier
347 _atext file
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490 1 _aContributions to Statistics,
_x2628-8966
505 0 _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.
520 _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.
650 0 _aStatistics.
_965982
650 0 _aRegression analysis.
_961412
650 0 _aStatistics .
_965982
650 0 _aMachine learning.
_955059
650 0 _aBiometry.
_965981
650 1 4 _aStatistical Theory and Methods.
650 2 4 _aLinear Models and Regression.
_976680
650 2 4 _aBayesian Inference.
_976436
650 2 4 _aStatistical Learning.
_967991
650 2 4 _aBiostatistics.
_965985
700 1 _aEinbeck, Jochen.
_eeditor.
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700 1 _aMaeng, Hyeyoung.
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700 1 _aOgundimu, Emmanuel.
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_980439
700 1 _aPerrakis, Konstantinos.
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_4http://id.loc.gov/vocabulary/relators/edt
_980440
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031657221
776 0 8 _iPrinted edition:
_z9783031657245
776 0 8 _iPrinted edition:
_z9783031657252
830 0 _aContributions to Statistics,
_x2628-8966
_980441
856 4 0 _uhttps://doi.org/10.1007/978-3-031-65723-8
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
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