Abstract
In this paper, we consider the estimation of the finite time survival probability in the classical risk
model when the initial surplus is zero. We construct a nonparametric estimator by Fourier inversion and kernel
density estimation method. Under some mild assumptions imposed on the kernel, bandwidth and claim size
density, we derive the order of the bias and variance, and show that the estimator has asymptotic normality
property. Some simulation studies show that the estimator performs quite well in the finite sample setting.
model when the initial surplus is zero. We construct a nonparametric estimator by Fourier inversion and kernel
density estimation method. Under some mild assumptions imposed on the kernel, bandwidth and claim size
density, we derive the order of the bias and variance, and show that the estimator has asymptotic normality
property. Some simulation studies show that the estimator performs quite well in the finite sample setting.
| Original language | English |
|---|---|
| Pages (from-to) | 739-754 |
| Journal | Acta Mathematicae Applicatae Sinica |
| Volume | 32 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2016 |
| Externally published | Yes |