Authors: | Chown, Justin Müller, Ursula U. |
Title: | Detecting heteroskedasticity in nonparametric regression using weighted empirical processes |
Language (ISO): | en |
Abstract: | Heteroskedastic errors can lead to inaccurate statistical conclusions if they are not properly handled. We introduce a test for heteroskedasticity for the nonparametric regression model with multiple covariates. It is based on a suitable residual-based empirical distribution function. The residuals are constructed using local polynomial smoothing. Our test statistic involves a "detection function" that can verify heteroskedasticity by exploiting just the independence-dependence structure between the detection function and model errors, i.e. we do not require a specific model of the variance function. The procedure is asymptotically distribution free: inferences made from it do not depend on unknown parameters. It is consistent at the parametric (root-n) rate of convergence. Our results are extended to the case of missing responses and illustrated with simulations. |
Subject Headings: | heteroskedastic nonparametric regression weighted empirical process transfer principle missing at random local polynomial smoother |
Subject Headings (RSWK): | Heteroskedastizität Nichtparametrische Regression Statistischer Test |
URI: | http://hdl.handle.net/2003/35284 http://dx.doi.org/10.17877/DE290R-17327 |
Issue Date: | 2016 |
Appears in Collections: | Sonderforschungsbereich (SFB) 823 |
Files in This Item:
File | Description | Size | Format | |
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DP_5016_SFB823_Chown_Müller.pdf | DNB | 419.34 kB | Adobe PDF | View/Open |
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This item is protected by original copyright
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