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- W2548590935 abstract "Clustering is used by actuaries in a data compression process to make massive or nested stochastic simulations practical to run. A large data set of assets or liabilities is partitioned into a user-defined number of clusters, each of which is compressed to a single representative policy. The representative policies can then simulate the behavior of the entire portfolio over a large range of stochastic scenarios. Such processes are becoming increasingly important in understanding product behavior and assessing reserving requirements in a big-data environment. This article proposes a variety of clustering techniques that can be used for this purpose. Initialization methods for performing clustering compression are also compared, including principal components, factor analysis, and segmentation. A variety of methods for choosing a cluster's representative policy is considered. A real data set comprising variable annuity policies, provided by Milliman, is used to test the proposed methods. It is found that th..." @default.
- W2548590935 created "2016-11-11" @default.
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- W2548590935 date "2016-11-04" @default.
- W2548590935 modified "2023-09-23" @default.
- W2548590935 title "Model-Based and Nonparametric Approaches to Clustering for Data Compression in Actuarial Applications" @default.
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- W2548590935 doi "https://doi.org/10.1080/10920277.2016.1234398" @default.
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