Checking the Adequacy for A Distortion Errors-In-Variables Parametric Regression Model
Jun Zhang, Gaorong Li, Zhenghui Feng
Computational Statistics and Data Analysis 83 (2015) 52–64
#002269 20160218 ()
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

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