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• W2949393797 abstract "Point and interval estimation of future disability inception and recovery rates are predominantly carried out by combining generalized linear models (GLM) with time series forecasting techniques into a two-step method involving parameter estimation from historical data and subsequent calibration of a time series model. This approach may in fact lead to both conceptual and numerical problems since any time trend components of the model are incoherently treated as both model parameters and realizations of a stochastic process. We suggest that this general two-step approach can be improved in the following way: First, we assume a stochastic process form for the time trend component. The corresponding transition densities are then incorporated into the likelihood, and the model parameters are estimated using the Expectation-Maximization algorithm. We illustrate the modelling procedure by fitting the model to Swedish disability claims data." @default.
• W2949393797 created "2019-06-27" @default.
• W2949393797 creator A5042490484 @default.
• W2949393797 creator A5082148376 @default.
• W2949393797 date "2014-12-23" @default.
• W2949393797 modified "2023-10-01" @default.
• W2949393797 title "A hidden Markov approach to disability insurance" @default.
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• W2949393797 hasPublicationYear "2014" @default.
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