Nonparametric Estimation of the Laplace Transform from Empirical Data Using Symmetric Kernels Method
Overview & Synopsis
Focusing on the use of a Gaussian kernel, the paper constructs a nonparametric Laplace transform estimator via symmetric kernel methodology. Convergence rates and an asymptotic mean integrated squared error (AMISE) formula are used to establish theoretical properties of the estimator and guide optimal bandwidth choice. Finite-sample performance is evaluated through comprehensive simulations. Real and synthetic data applications illustrate practical utility. Estimator robustness and accuracy are showed in outcomes, interpretation the implement valuable across statistical and practical fields.
Indexed from repository: HAL Open Science
(
hal.science)
Keyboard Shortcuts:
← / → for pages • + / - for zoom • 0 to fit width
Powered by Mozilla PDF.js v3.11
Document Information
| Document Title | Nonparametric Estimation of the Laplace Transform from Empirical Data Using Symmetric Kernels Method |
|---|---|
| Author / Organization | Raogo Frank Emile Er Jumeau Kabore, Sobom Matthieu Some, Abel Zongo, S Pierre Clovis Nitiema |
| Subject & Discipline | Multidisciplinary Academic Open Research |
| Categories | Science & Mathematics |
| Publication Year | 2025 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
HAL Open Science
(hal.science)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-254635 |
| Original Source Link | Visit Source Page on hal.science » |