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- W4385261581 abstract "Model checking is essential to evaluate the adequacy of statistical models and the validity of inferences drawn from them. Particularly, hierarchical models such as latent Gaussian models (LGMs) pose unique challenges as it is difficult to check assumptions about the distribution of the latent parameters. Discrepancy measures are often used to quantify the degree to which a model fit deviates from the observed data. We construct discrepancy measures by (a) defining an alternative model with relaxed assumptions and (b) deriving the discrepancy measure most sensitive to discrepancies induced by this alternative model. We also promote a workflow for model criticism that combines model checking with subsequent robustness analysis. As a result, we obtain a general recipe to check assumptions in LGMs and the impact of these assumptions on the results. We demonstrate the ideas by assessing the latent Gaussianity assumption, a crucial but often overlooked assumption in LGMs. We illustrate the methods via examples utilising Stan and provide functions for easy usage of the methods for general models fitted through R-INLA." @default.
- W4385261581 created "2023-07-26" @default.
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- W4385261581 date "2023-07-23" @default.
- W4385261581 modified "2023-09-28" @default.
- W4385261581 title "Robustness, model checking and latent Gaussian models" @default.
- W4385261581 doi "https://doi.org/10.48550/arxiv.2307.12365" @default.
- W4385261581 hasPublicationYear "2023" @default.
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