Informed censoring: The parametric combination of data and expert information
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Informed censoring : The parametric combination of data and expert information. / Albrecher, Hansjörg; Bladt, Martin.
In: Journal of Statistical Planning and Inference, Vol. 233, 106171, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Informed censoring
T2 - The parametric combination of data and expert information
AU - Albrecher, Hansjörg
AU - Bladt, Martin
N1 - Publisher Copyright: © 2024 The Authors
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Expert information
KW - Informed censoring
KW - Likelihood methods
KW - Parametric inference
U2 - 10.1016/j.jspi.2024.106171
DO - 10.1016/j.jspi.2024.106171
M3 - Journal article
AN - SCOPUS:85189427329
VL - 233
JO - Journal of Statistical Planning and Inference
JF - Journal of Statistical Planning and Inference
SN - 0378-3758
M1 - 106171
ER -
ID: 388874055