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A simple deconvolving Kernel density estimator when noise is Gaussian
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Autores
Orientador(es)
Resumo(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
Descrição
Palavras-chave
Deconvolution Density Estimation Errors-In-Variables Kernel Simulations
Contexto Educativo
Citação
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).
Editora
Springer