| 杂志 | Computational Statistics and D
83 (2015) 52–64 |
| 作者 | Jun Zhang, Gaorong Li, Zhenghui Feng |
| 正文 | 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. |
| JEL-Codes： | |
| 关键词： | Confounding variables Errors-in-variables Distorting functions Empirical process |
