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- W2580492832 abstract "We consider the problem of estimating a regression function when a covariate is measured with error. Using the local polynomial estimator of Delaigle et al. [(2009), ‘A Design-adaptive Local Polynomial Estimator for the Errors-in-variables Problem’, Journal of the American Statistical Association, 104, 348–359] as a benchmark, we propose an alternative way of solving the problem without transforming the kernel function. The asymptotic properties of the alternative estimator are rigorously studied. A detailed implementing algorithm and a computationally efficient bandwidth selection procedure are also provided. The proposed estimator is compared with the existing local polynomial estimator via extensive simulations and an application to the motorcycle crash data. The results show that the new estimator can be less biased than the existing estimator and is numerically more stable." @default.
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- W2580492832 date "2017-03-15" @default.
- W2580492832 modified "2023-10-16" @default.
- W2580492832 title "An alternative local polynomial estimator for the error-in-variables problem" @default.
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- W2580492832 doi "https://doi.org/10.1080/10485252.2017.1303060" @default.
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