Bias Reduction and Elimination with Kernel Estimators
نویسنده
چکیده
with Kernel Estimators Stephan R. Sain1 De ember 8, 2000 SUMMARY: A great deal of resear h has fo used on improving the bias properties of kernel estimators. One proposal involves removing the restri tion of non-negativity on the kernel to onstru t \higher-order" kernels that eliminate additional terms in the Taylor's series expansion of the bias. This paper onsiders an alternative that uses a lo al approa h to bandwidth sele tion to not only redu e the bias, but to eliminate it entirely. These soalled \zero-bias bandwidths" are shown to exist for univariate and multivariate kernel density estimation as well as kernel regression. Impli ations of the existen e of su h bandwidths are dis ussed. An estimation strategy is presented, and the extent of the redu tion or elimination of bias in pra ti e is studied through simulation and example.
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