Authors: | Bissantz, Nicolai Chown, Justin Dette, Holger |
Title: | Regularization parameter selection in indirect regression by residual based bootstrap |
Language (ISO): | en |
Abstract: | 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. |
Subject Headings: | bandwidth selection smooth bootstrap residual-based empirical distribution function regularization indirect nonparametric regression deconvolution function estimator |
Subject Headings (RSWK): | Nichtparametrische Regression Bootstrap-Statistik |
URI: | http://hdl.handle.net/2003/35302 http://dx.doi.org/10.17877/DE290R-17345 |
Issue Date: | 2016 |
Appears in Collections: | Sonderforschungsbereich (SFB) 823 |
Files in This Item:
File | Description | Size | Format | |
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DP_5616_SFB823_Bissantz_Chown_Dette.pdf | DNB | 433.23 kB | Adobe PDF | View/Open |
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This item is protected by original copyright
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