| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 187.86 KB | Adobe PDF |
Authors
Advisor(s)
Abstract(s)
Deconvolving kernel estimators when noise is Gaussian entail heavy calculations. In order to obtain the density estimates numerical evaluation of a specific integral is needed. This work proposes an approximation to the deconvolving kernel which simplifies considerably calculations by avoiding the typical numerical integration. Simulations included indicate that the lost in performance relatively to the true deconvolving kernel, is almost negligible in finite samples
Description
Keywords
Deconvolution Density Estimation Errors-In-Variables Kernel Simulations
Pedagogical Context
Citation
Proença, Isabel .(2006). “A simple deconvolving Kernel density estimator when noise is Gaussian” in The Art of Semiparametrics , Contributions to Statistics Series, Physica-Verlag. S. Sperlich, W. Härdle and G. Aydinli (eds). Physica-Verlag: pp. 22-31 . (Search in 2023).
Publisher
Springer