| Name : R-ADLP
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| Version : 0.1.0
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.2.2
| Date : 2024-06-14 10:56:52
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| Group : Development/Libraries/Other
| Source RPM : R-ADLP-0.1.0-lp153.2.2.src.rpm
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| Size : 1.13 MB
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| Packager : (none)
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| Summary : Accident and Development Period Adjusted Linear Pools for Actuarial Stochastic Reserving
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Description :
Loss reserving generally focuses on identifying a single model that can
generate superior predictive performance. However, different loss
reserving models specialise in capturing different aspects of loss
data. This is recognised in practice in the sense that results from
different models are often considered, and sometimes combined. For
instance, actuaries may take a weighted average of the prediction
outcomes from various loss reserving models, often based on subjective
assessments. This package allows for the use of a systematic framework
to objectively combine (i.e. ensemble) multiple stochastic loss
reserving models such that the strengths offered by different models
can be utilised effectively. Our framework is developed in Avanzi et
al. (2023). Firstly, our criteria model combination considers the full
distributional properties of the ensemble and not just the central
estimate - which is of particular importance in the reserving context.
Secondly, our framework is that it is tailored for the features
inherent to reserving data. These include, for instance, accident,
development, calendar, and claim maturity effects. Crucially, the
relative importance and scarcity of data across accident periods
renders the problem distinct from the traditional ensemble techniques
in statistical learning. Our framework is illustrated with a complex
synthetic dataset. In the results, the optimised ensemble outperforms
both (i) traditional model selection strategies, and (ii) an equally
weighted ensemble. In particular, the improvement occurs not only with
central estimates but also relevant quantiles, such as the 75th
percentile of reserves (typically of interest to both insurers and
regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) \"Ensemble
distributional forecasting for insurance loss reserving\"
< doi:10.48550/arXiv.2206.08541>.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
