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局部加性估计AI 解读

Summary Additive models are popular in high dimensional regression problems owing to their flexibility in model building and optimality in additive function estimation. Moreover, they do not suffer from the so-called curse of dimensionality generally arising in non-parametric regression settings. Less known is the model bias that is incurred from the restriction to the additive class of models. We introduce a new class of estimators that reduces additive model bias, yet…

局部多项式的有限样本方差：分析与解决方法AI 解读

Fitting local polynomials in nonparametric regression has a number of advantages. The attractive theoretical features are in a partial contradiction to variance properties for random design and to practical experience over a broad range of situations. No upper bound can be given for the conditional variance. The unconditional variance is infinite when using optimal weights with compact support. Properties are better for Gaussian weights. We analyze local polynomials for…

重新审视残差方差的非参数估计AI 解读

Several difference-based estimators of residual variance are compared for finite sample size. Since the introduction of a rather simple estimator by Gasser, Sroka & Jennen-Steinmetz (1986) other proposals have been made. Here the one given by Hall, Kay & Titterington (1990) is of particular interest. It minimizes the asymptotic variance. Unfortunately it has severe problems with finite sample bias, and the estimator of Gasser et al. (1986) proves still to be a good choice. A…

重新审视残差方差的非参数估计AI 解读

Several difference-based estimators of residual variance are compared for finite sample size. Since the introduction of a rather simple estimator by Gasser, Sroka & Jennen-Steinmetz (1986) other proposals have been made. Here the one given by Hall, Kay & Titterington (1990) is of particular interest. It minimizes the asymptotic variance. Unfortunately it has severe problems with finite sample bias, and the estimator of Gasser et al. (1986) proves still to be a good choice. A…