Automatic, computer aided geometric design of free-knot, regression splines

A new algorithm for Computer Aided Geometric Design of least squares (LS) splines with variable knots, named GeDS, is presented. It is based on interpreting functional spline regression as a parametric B-spline curve, and on using the shape preserving property of its control polygon. The GeDS algorithm includes two major stages. For the first stage, an automatic adaptive, knot location algorithm is developed. By adding knots, one at a time, it sequentially "breaks" a straight line segment into pieces in order to construct a linear LS B-spline fit, which captures the "shape" of the data. A stopping rule is applied which avoids both over and under fitting and selects the number of knots for the second stage of GeDS, in which smoother, higher order (quadratic, cubic, etc.) fits are generated. The knots appropriate for the second stage are determined, according to a new knot location method, called the averaging method. It approximately preserves the linear precision property of B-spline curves and allows the attachment of smooth higher order LS B-spline fits to a control polygon, so that the shape of the linear polygon of stage one is followed. The GeDS method produces simultaneously linear, quadratic, cubic (and possibly higher order) spline fits with one and the same number of B-spline regression functions. The GeDS algorithm is very fast, since no deterministic or stochastic knot insertion/deletion and relocation search strategies are involved, neither in the first nor the second stage. Extensive numerical examples are provided, illustrating the performance of GeDS and the quality of the resulting LS spline fits. The GeDS procedure is compared with other existing variable knot spline methods and smoothing techniques, such as SARS, HAS, MDL, AGS methods and is shown to produce models with fewer parameters but with similar goodness of fit characteristics, and visual quality

Bootstrap Estimation of the Predictive Distributions of Reserves using Paid and Incurred Claims

This paper presents a bootstrap approach to estimate the prediction distributions of reserves produced by the Munich chain ladder (MCL) model. The MCL model was introduced by Quarg and Mack (2004) and takes into account both paid and incurred claims information. In order to produce bootstrap distributions, this paper addresses the application of bootstrapping methods to dependent data, with the consequence that correlations are considered. Numerical examples are provided to illustrate the algorithm and the prediction errors are compared for the new bootstrapping method applied to MCL and a more standard bootstrapping method applied to the chain-ladder technique

A fuzzy approach to grouping by policyholder age in general insurance

In general insurance, policyholder age is often treated as a factor with the number of levels requiring that the individual ages of the policyholders be grouped. Although the groups are usually defined by the existing underwriting structure, it should be investigated as part of any premium rating exercise that uses a model to assess past claims experience. It is possible that an incorrect grouping by policyholder age could bias the results of the risk premium estimation. On the other hand, it may not be computationally feasible to use separate ages in the premium model, making some form of grouping necessary. In this paper, we specify a data-based procedure for grouping by age using fuzzy set theory. An example is given that illustrates how the method can be used in practice

The need for theory in actuarial economic models

ABSTRACTThis paper addresses the fundamental issues in the construction and use of actuarial economic models, with specific reference to those described in the UK literature. Two approaches are considered: an empirical approach and a theoretical approach using financial economics. Although empirical testing is essential, the difficulties associated with it should not be underestimated. A theoretical framework can be used to limit the impact of these difficulties. However, economic modelling is further complicated by the lack of a reliable and comprehensive theoretical framework. This suggests that economic models are always likely to be inaccurate and consequently actuarial judgement is likely to be indispensable.</jats:p

Automated Graduation using Bayesian Trans-dimensional Models

This paper presents a new method of graduation which uses parametric formulae together with Bayesian reversible jump Markov chain Monte Carlo methods. The aim is to provide a method which can be applied to a wide range of data, and which does not require a lot of adjustment or modification. The method also does not require one particular parametric formula to be selected: instead, the graduated values are a weighted average of the values from a range of formulae. In this way, the new method can be seen as an automatic graduation method which we believe that in many cases can be applied without any adjustments and provide satisfactory graduated values

Modelling Claims Run-off with Reversible Jump Markov Chain Monte Carlo Methods

In this paper we describe a new approach to modelling the development of claims run-off triangles. This method replaces the usual adhoc practical process of extrapolating a development pattern to obtain tail factors with an objective procedure. An example is given, illustrating the results in a practical context, and the WinBUGS code is supplied

Reversible jump Markov chain Monte Carlo method for parameter reduction in claims reserving

We present an application of the reversible jump Markov chain Monte Carlo (RJMCMC) method to the important problem of setting claims reserves in general insurance business for the outstanding loss liabilities. A measure of the uncertainty in these claims reserves estimates is also needed for solvency purposes. The RJMCMC method described in this paper represents an improvement over the manual processes often employed in practice. In particular, our RJMCMC method describes parameter reduction and tail factor estimation in the claims reserving process, and, moreover, it provides the full predictive distribution of the outstanding loss liabilities