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Credibility Models with Time-Varying Trend Components

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TLDR
This paper uses an automobile insurance example to illustrate how a method that allows for time-varying parameters in the process, yet still provides the shrinkage needed for sound ratemaking can be accomplished.
Abstract
Traditional credibility models have treated the process generating the losses as stable over time, perhaps with a deterministic trend imposed. However, there is ample evidence that these processes are not stable over time. What is required is a method that allows for time-varying parameters in the process, yet still provides the shrinkage needed for sound ratemaking. In this paper we use an automobile insurance example to illustrate how this can be accomplished.

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Dissertation

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References
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TL;DR: In this paper, a unified treatment of linear and nonlinear filtering theory for engineers is presented, with sufficient emphasis on applications to enable the reader to use the theory for engineering problems.
Journal ArticleDOI

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Statistical methods for forecasting

TL;DR: The Regression Model and Its Application in Forecasting and Relationships Between Forecasts from General Exponential Smoothing and Forecast from Arima Time Series Models are studied.
Journal ArticleDOI

Understanding the Kalman Filter

TL;DR: It is shown how the successfully used Kalman filter can be easily understood by statisticians if the authors use a Bayesian formulation and some well-known results in multivariate statistics.
Journal ArticleDOI

Evaluation of likelihood functions for Gaussian signals

TL;DR: Time-varying finite-dimensional Markov models are discussed as they lead to a direct mechanization for the required conditional expectation and a simple example of a multipath communication system is discussed and an explicit mechanization indicated.
Related Papers (5)
Trending Questions (1)
What is Time trend variable?

The paper does not explicitly define or discuss the concept of a "time trend variable."