Decompounding under Gaussian noise
| dc.creator | Gugushvili, Shota | |
| dc.date | 2007-11-05 | |
| dc.date.accessioned | 2026-07-07T08:40:47Z | |
| dc.date.available | 2026-07-07T08:40:47Z | |
| dc.description | Assuming that a stochastic process $X=(X_t)_{t\geq 0}$ is a sum of a compound Poisson process $Y=(Y_t)_{t\geq 0}$ with known intensity $λ$ and unknown jump size density $f,$ and an independent Brownian motion $Z=(Z_t)_{t\geq 0},$ we consider the problem of nonparametric estimation of $f$ from low frequency observations from $X.$ The estimator of $f$ is constructed via Fourier inversion and kernel smoothing. Our main result deals with asymptotic normality of the proposed estimator at a fixed point. | |
| dc.description | 26 pages, 6 figures | |
| dc.identifier | https://arxiv.org/abs/0711.0719 | |
| dc.identifier | http://arxiv.org/abs/0711.0719 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/141472 | |
| dc.subject | Statistics Theory | |
| dc.subject | 62G07; 62G20 | |
| dc.title | Decompounding under Gaussian noise | |
| dc.type | text |