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基于时间齐次高质量标记信息的未来条件风险率超高效估计AI 解读

Summary We introduce a new concept for forecasting future events based on marker information. The model is developed in the nonparametric counting process setting under the assumptions that the marker is of so-called high quality and with a time-homogeneous conditional distribution. Despite the model having nonparametric parts, it is established herein that it attains a parametric rate of uniform consistency and uniform asymptotic normality. In usual nonparametric scenarios,…

广义结构模型中的回代检验AI 解读

Summary We introduce bootstrap tests for semiparametric generalized structured models. These can be used for testing different kinds of model specifications like separability, functional forms and homogeneity of effects, or for performing variable selection in a large class of semiparametric models. The test statistics are based on the comparison of non- and semiparametric alternatives in which both the null hypothesis and the alternative are non- or semiparametric. All…

以设计点局部重心重新定位的核平滑器AI 解读

The local linear smoother usually gives better performance than the Nadaraya-Watson smoother. An exception is the case of data sparsity. Here we discuss a modification of the Nadaraya-Watson smoother by Müller & Song (1993), based on a horizontal shift of the kernel weights towards the local centre of mass of the design points. This gives performance similar to the local linear when that works well and better performance when it does not. The new smoother also preserves…