Fitting a parametric distribution for large claims in case of censored or partitioned data

Weba, Michael

doi:10.1016/0167-6687(93)90825-a

Cited by 1 publication

(2 citation statements)

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“…To avoid the MLE nesting problems, discussed in the previous section, we will focus on the latter. This approach has also found its way into the actuarial literature, see Beirlant and Teugels (1992), Weba (1993), and Beirlant et al (1994). We will therefore restrict the exposition of the underlying theory to its main features.…”

Section: Residual Life and Tail Parametersmentioning

confidence: 99%

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Mixtures of tails in clustered automobile collision claims

Kalb

Kofman

Vorst

1996

Insurance: Mathematics and Economics

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Knowledge of the tail shape of claim distributions provides important actuarial information. This paper discusses how two techniques commonly used in assessing the most appropriate underlying distribution can be usefully combined. The maximum likelihood approach is theoretically appealing since it is preferable to many other estimators in the sense of best asymptotic normality. Likelihood based tests are, however, not always capable to discriminate among non-nested classes of distributions. Extremal value theory offers an attractive tool to overcome this problem. It shows that a much larger set of distributions is nested in their tails by the so-called tail parameter. This paper shows that both estimation strategies can be usefully combined when the data generating process is characterized by strong clustering in time and size. We find that the extreme value theory is a useful starting point in detecting the appropriate distribution class. Once that has been achieved, the likelihood-based EM-algorithm is proposed to capture the clustering phenomena. Clustering is particularly pervasive in actuarial data. An empirical application to a four-year data set of Dutch automobile collision claims is therefore used to illustrate the approach.

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Section: Residual Life and Tail Parametersmentioning

confidence: 99%

“…First, we generate some large number of daily frequencies according to (time dependent adjustments of) Eqs. (23) and (24). Next, we generate severities for each generated frequency.…”

Section: Compounding Claims Distributionsmentioning

confidence: 99%