New methods for bias correction at endpoints and boundaries
We suggest two new, translation-based methods for estimating and correcting for bias when estimating the edge of a distribution. The first uses an empirical translation applied to the argument of the kernel, in order to remove the main effects of the asymmetries that are inherent when constructing estimators at boundaries. Placing the translation inside the kernel is in marked contrast to traditional approaches, such as the use of high-order kernels, which are related to the jackknife and, in...[Show more]
|Collections||ANU Research Publications|
|Source:||The Annals of Statistics|
|01_Hall_New_Methods_2002.pdf||209.02 kB||Adobe PDF|
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