Nonparametric modal regression in the presence of measurement error
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| Authors | Haiming Zhou, Xianzheng Huang |
| Journal/Conference Name | Electronic Journal of Statistics |
| Paper Category | Other |
| Paper Abstract | In the context of regressing a response $Y$ on a predictor $X$, we consider estimating the local modes of the distribution of $Y$ given $X=x$ when $X$ is prone to measurement error. We propose two nonparametric estimation methods, with one based on estimating the joint density of $(X, Y)$ in the presence of measurement error, and the other built upon estimating the conditional density of $Y$ given $X=x$ using error-prone data. We study the asymptotic properties of each proposed mode estimator, and provide implementation details including the mean-shift algorithm for mode seeking and bandwidth selection. Numerical studies are presented to compare the proposed methods with an existing mode estimation method developed for error-free data naively applied to error-prone data. |
| Date of publication | 2016 |
| Code Programming Language | R |
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