Low order approximations in deconvolution and regression with errors in variables
We suggest two new methods, which are applicable to both deconvolution and regression with errors in explanatory variables, for nonparametric inference. The two approaches involve kernel or orthogonal series methods. They are based on defining a low order approximation to the problem at hand, and proceed by constructing relatively accurate estimators of that quantity rather than attempting to estimate the true target functions consistently. Of course, both techniques could be employed to...[Show more]
|Collections||ANU Research Publications|
|Source:||Journal of the Royal Statistical Society Series B|
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