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迭代变换与核密度估计AI 解读

Transformation from a parametric family can improve the performance of kernel density estimation. In this article we give two data-driven estimators for the optimal transformation parameter. We demonstrate that multiple families of transformations can be used at the same time, and there can be benefits to iterating this process. The transformation scheme can be expected to first pick the right transformation family and then pick the optimal parameter. Insight as to the…

迭代变换核密度估计AI 解读

Transformation from a parametric family can improve the performance of kernel density estimation.In this paper, we give two data-driven estimators for the optimal transformation parameter.We demonstrate that multiple families of transformations can be employed at the same time, and there can be bene ts to iterating this process.The transformation scheme can be expected to rst pick the right transformation family and then the optimal parameter.Insight as to the performance of…

带宽选择的最优可达常数AI 解读

For the data based choice of the bandwidth of a kernel density estimator, several methods have recently been proposed which have a very fast asymptotic rate of convergence to the optimal bandwidth. In particular the relative rate of convergence is the square root of the sample size, which is known to be the best possible. The point of this paper is to show how semiparametric arguments can be employed to calculate the best possible constant coefficient, that is, an analog of…