Goodness-of-fit tests in linear EV regression with replications
type: Item
instance of: scholarly article
title: Goodness-of-fit tests in linear EV regression with replications
zbMATH Open document ID: 1398.62093
published in: Metrika
publication date: 2018-06-13T00:00:00Z
review text: The authors consider an error-in-variables linear regression model, i.e. the regressor \(X\) is observed via the surrogate \(Z = X + U\), i.e., besides the regression error there is the measurement error \(U\). The authors construct a goodness-of-fit test for the density function of the regression error under the assumption that the distribution of \(U\) is unknown. However, replications of the surrogates \(Z\) are available and are used to construct a nonparametric estimate of the density of \(U\). The test statistics is a weighted integrated squared distance between a nonparametric estimator and a semi-parametric estimator of the density functions of certain residuals. Asymptotic normality under the null hypothesis as well as consistency and local power results are proven. The finite sample behaviour is evaluated via simulation studies.
reviewed by: Wolfgang Näther
Mathematics Subject Classification ID: 62G08
Mathematics Subject Classification ID: 62G10
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62G07
Mathematics Subject Classification ID: 62G20
zbMATH DE Number: 6887306
zbMATH Keywords: error-in-variables regression
zbMATH Keywords: replication
zbMATH Keywords: goodnes-of-fit test for error density functions
zbMATH Keywords: consistency and local power
author: Weijia Jia
author: Weixing Song
MaRDI profile type: Publication
full work available at URL: s00184-018-0648-1
OpenAlex ID: W2793895060
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DOI: 10.1007/S00184-018-0648-1
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description: scientific article; zbMATH DE number 6887306