Empirical likelihood-based dimension reduction inference for linear error-in-responses models with validation study

Abstract

In this paper, linear errors-in-response models are considered in the presence of validation data on the responses. A semiparametric dimension reduction technique is employed to define an estimator of β with asymptotic normality, the estimated empirical loglikelihoods and the adjusted empirical loglikelihoods for the vector of regression coefficients and linear combinations of the regression coefficients, respectively. The estimated empirical log-likelihoods are shown to be asymptotically distributed as weighted sums of independent χ1 and the adjusted empirical loglikelihoods are proved to be asymptotically distributed as standard chi-squares, respectively. A simulation study is conducted to compare the proposed methods in terms of coverage accuracies and average lengths of the confidence intervals.

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Cite this paper

@inproceedings{Wang2002EmpiricalLD, title={Empirical likelihood-based dimension reduction inference for linear error-in-responses models with validation study}, author={Qihua Wang and Wolfgang K. H{\"a}rdle}, year={2002} }