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Using SIMEX for smoothing-parameter choice in errors-in-variables problems
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DOI：10.1198/016214507000001355.png)
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Abstract
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SIMEX methods are attractive for solving curve estimation problems in errors-in-variables regression, using parametric or semiparametric techniques. However, nonparametric approaches are generally of quite a different type, being based on, for example, kernels, local-linear modeling, ridging, orthogonal series, or splines. All of these techniques involve the challenging (and not well studied) issue of empirical smoothing parameter choice. We show that SIMEX can be used effectively for selecting smoothing parameters when applying nonparametric methods to errors-in-variable regression. In particular, we suggest an approach based on multiple error-inflated (or remeasured) data sets and extrapolation.
Keywords:
bandwidth
bootstrap
cross-validation
ill-posed problem
inverse problem
kernel estimation
Monte Carlo simulation
nonparametric curve estimation
nonparametric regression
parametric model
statistical smoothing
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