Informed censoring: The parametric combination of data and expert information

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The statistical censoring setup is extended to the situation when random measures can be assigned to the realization of datapoints, leading to a new way of incorporating expert information into the usual parametric estimation procedures. The asymptotic theory is provided for the resulting estimators, and some special cases of practical relevance are studied in more detail. Although the proposed framework mathematically generalizes censoring and coarsening at random, and borrows techniques from M-estimation theory, it provides a novel and transparent methodology which enjoys significant practical applicability in situations where expert information is present. The potential of the approach is illustrated by a concrete actuarial application of tail parameter estimation for a heavy-tailed MTPL dataset with limited available expert information.

Original languageEnglish
Article number106171
JournalJournal of Statistical Planning and Inference
Number of pages16
Publication statusPublished - 2024

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© 2024 The Authors

    Research areas

  • Expert information, Informed censoring, Likelihood methods, Parametric inference

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