Checking the adequacy for a distortion errors-in-variables parametric regression model

Jun Zhang, Gaorong Li and Zhenghui Feng

Computational Statistics & Data Analysis, 2015, vol. 83, issue C, 52-64

Abstract: This paper studies tools for checking the validity of a parametric regression model, when both response and predictors are unobserved and distorted in a multiplicative fashion by an observed confounding variable. A residual based empirical process test statistic marked by proper functions of the regressors is proposed. We derive asymptotic distribution of the proposed empirical process test statistic: a centered Gaussian process under the null hypothesis and a non-centered one under local alternatives converging to the null hypothesis at parametric rates. We also suggest a bootstrap procedure to calculate critical values. Simulation studies are conducted to demonstrate the performance of the proposed test statistic and real examples are analyzed for illustrations.

Keywords: Confounding variables; Errors-in-variables; Distorting functions; Empirical process (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:83:y:2015:i:c:p:52-64

DOI: 10.1016/j.csda.2014.09.018

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