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

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书目名称Developments in Statistical Modelling影响因子(影响力)学科排名




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书目名称Developments in Statistical Modelling网络公开度学科排名




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书目名称Developments in Statistical Modelling被引频次学科排名




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书目名称Developments in Statistical Modelling年度引用学科排名




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书目名称Developments in Statistical Modelling读者反馈学科排名

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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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