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A kernel type nonparametric density estimator for decompounding

Bert van Es·Shota Gugushvili·Peter Spreij
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Abstract

Given a sample from a discretely observed compound Poisson process, we consider estimation of the density of the jump sizes. We propose a kernel type nonparametric density estimator and study its asymptotic properties. An order bound for the bias and an asymptotic expansion of the variance of the estimator are given. Pointwise weak consistency and asymptotic normality are established. The results show that, asymptotically, the estimator behaves very much like an ordinary kernel estimator.

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Bert van Es, Shota Gugushvili, Peter Spreij. 2007-09-05. A kernel type nonparametric density estimator for decompounding. https://doi.org/10.3150/07-bej6091

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