Regularization parameter selection in indirect regression by residual based bootstrap

Autor:	Bissantz, Nicolai, Chown, Justin, Dette, Holger
Jazyk:	angličtina
Rok vydání:	2016
Předmět:	indirect nonparametric regression deconvolution function estimator residual-based empirical distribution function regularization Bootstrap-Statistik Nichtparametrische Regression smooth bootstrap bandwidth selection
Popis:	Residual-based analysis is generally considered a cornerstone of statistical methodology. For a special case of indirect regression, we investigate the residual-based empirical distribution function and provide a uniform expansion of this estimator, which is also shown to be asymptotically most precise. This investigation naturally leads to a completely data-driven technique for selecting a regularization parameter used in our indirect regression function estimator. The resulting methodology is based on a smooth bootstrap of the model residuals. A simulation study demonstrates the effectiveness of our approach. Discussion Paper / SFB823;56, 2016
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eda6123bc467c9589b844a275fe75af0 http://hdl.handle.net/2003/35302 Zobrazit plný text záznamu