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Showing 1 to 2 of 2 for “"Information Theory and Statistics"”.
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ROBUST KULLBACK-LEIBLER DIVERGENCE AND ITS APPLICATIONS IN UNIVERSAL HYPOTHESIS TESTING AND DEVIATION DETECTION
… is one of the most fundamental metrics in information theory and statistics and provides various operational interpretations in the context of mathematical communication theory and statistical hypothesis testing. The KL divergence for discrete distributions has the desired continuity …
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Essays on Model Selection Uncertainty and Model Averaging: Computational and Empirical Work with Beta Regression, Multiple Linear Regression with ARMA Innovations, and the Minimum Description Length Principle
Uncertainty in model selection is under-explored and frequently resolved non-rigorously through beliefs about generalizability, practical usefulness, and computational ease. This is problematic as model selection routinely admits multiple models which imposes extra uncertainty on all post-selection …