A typicality principle that penalizes parameter values under which observed data look atypical is shown to fix maximum likelihood failures in three examples and to yield calibrated plausibility regions.
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The typicality principle and its implications for statistics and data science
A typicality principle that penalizes parameter values under which observed data look atypical is shown to fix maximum likelihood failures in three examples and to yield calibrated plausibility regions.