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Titlebook: Developments in Statistical Modelling; Jochen Einbeck,Hyeyoung Maeng,Konstantinos Perraki Conference proceedings 2024 The Editor(s) (if ap

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書(shū)目名稱(chēng)Developments in Statistical Modelling
編輯Jochen Einbeck,Hyeyoung Maeng,Konstantinos Perraki
視頻videohttp://file.papertrans.cn/285/284616/284616.mp4
概述Provides a snapshot of the current developments in statistical modelling.Covers a wide range of topics and applications, particularly from biostatistics.Brings together researchers and practitioners w
叢書(shū)名稱(chēng)Contributions to Statistics
圖書(shū)封面Titlebook: Developments in Statistical Modelling; Jochen Einbeck,Hyeyoung Maeng,Konstantinos Perraki Conference proceedings 2024 The Editor(s) (if ap
描述.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 mode
出版日期Conference proceedings 2024
關(guān)鍵詞Statistical Modelling; Generalized Linear Models; Mixture Models; Regularization; Bayesian Inference; Spa
版次1
doihttps://doi.org/10.1007/978-3-031-65723-8
isbn_softcover978-3-031-65725-2
isbn_ebook978-3-031-65723-8Series ISSN 1431-1968 Series E-ISSN 2628-8966
issn_series 1431-1968
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Jochen Einbeck,Hyeyoung Maeng,Konstantinos PerrakiProvides a snapshot of the current developments in statistical modelling.Covers a wide range of topics and applications, particularly from biostatistics.Brings together researchers and practitioners w
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,Shrinkage in?a?Bayesian Panel Data Model with?Time-Varying Coefficients,n approach with priors that allow shrinkage to constant and zero effects as well as to simpler dependence structures. The model is evaluated in a simulation study and applied to the analysis of yearly earnings of mothers in Austria who returned to the labour market after maternity leave.
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to a mixed model; in the new model it is shown that a more direct method can be used keeping the sparse structure of P-splines. The method is illustrated with a two-dimensional example using the R-package . on CRAN. We will show that for this example . is several orders of magnitude faster than othe
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Bayesian method, referred to as the ordinal structural expectation maximization (OSEM) method. Both methods assume that the ordinal variables originate from Gaussian variables, which can only be observed in discretized form, and that the dependencies in the unobserved latent Gaussian space can be de
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access to data from community engagement activities and social media content. However, novel analytic methods are required to process and analyse data in unstructured formats (e.g. transcripts, text and images) and to extract useful information for decision-making. This paper proposes an analytics
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