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Inference after model selection and averaging via confidence distributions and curves

Boek - Dissertatie

Model selection and model averaging are common practices to find the best model that explains the observed data. When the working model is selected using data-driven methods and the same data are used for inference about population parameters, guarantees of classical inference techniques might not hold anymore. This dissertation discusses ways of producing valid inference for post-selection and for model averaged estimators via confidence distributions and confidence curves. While classical inference concepts such as p-values, confidence intervals and point estimators can easily be read from a confidence distribution, it gives more information about the value of a parameter of interest than a single confidence interval or a 'single hypothesis' test. The first three chapters focus on how to obtain optimal post-selection conditional confidence distributions for possibly misspecified selected linear, generalized linear and linear mixed regression models. The fourth chapter provides a bootstrap approach to estimate the distribution of the more general model-averaged estimators in likelihood-based models.
Jaar van publicatie:2021
Toegankelijkheid:Open