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两样本问题的有效且自适应的非参数检验AI 解读

The notion of efficient test for a Euclidean parameter in a semiparametric model was introduced by Stein [Proc. Third Berkeley Symp. Math. Statist. Probab. 1 (1956) 187-195]. Such tests are locally most powerful for a wide class of infinite-dimensional nuisance parameters. The first formal application of this notion to a suitably parametrized two-sample problem was provided by Hájek [Ann. Math. Statist. 33 (1962) 1124-1147]. However, this and subsequent solutions appear to…

关于位置—尺度族平滑拟合优度检验的说明AI 解读

In this note, we show that the test statistic given by Rayner & Best (1986) for Neyman's (1937) smooth test of goodness of fit for location-scale families does not, in general, have the alleged chi-squared distribution. As a result, gross error may result from using their test strategy. The size of this error is obtained for the logistic distribution.

位置—尺度族平滑拟合优度检验的一则说明AI 解读

In this note, we show that the test statistic given by Rayner & Best (1986) for Neyman's (1937) smooth test of goodness of fit for location-scale families does not, in general, have the alleged chi-squared distribution. As a result, gross error may result from using their test strategy. The size of this error is obtained for the logistic distribution.

空间中位数与方向数据AI 解读

We introduce the normalized spatial median as an estimator of location for rotationally symmetric distributions on the hypersphere. We investigate some of its asymptotic properties and use them to obtain confidence regions for the modal direction of a distribution on the hypersphere. These results are then applied to the von Mises-Fisher distribution and to a contamination model. It is seen that the normalized spatial median can perform more efficiently than the normalized…

空间中位数与方向数据AI 解读

We introduce the normalized spatial median as an estimator of location for rotationally symmetric distributions on the hypersphere. We investigate some of its asymptotic properties and use them to obtain confidence regions for the modal direction of a distribution on the hypersphere. These results are then applied to the von Mises-Fisher distribution and to a contamination model. It is seen that the normalized spatial median can perform more efficiently than the normalized…

方向数据的 Bootstrap 置信锥AI 解读

Given a sample from a distribution F supported on a p-dimensional sphere, several confidence cones for the ‘mean directional vector’ are proposed. Some methods have been available, but they depend on parametric assumptions. In this paper, nonparametric confidence cones based on the bootstrap are considered, and consistency of these methods is obtained under minimal conditions. We compare the performances of bootstrap methods with the others by Monte Carlo simulations.

Given a sample from a distribution F supported on a p-dimensional sphere, several confidence cones for the ‘mean directional vector’ are proposed. Some methods have been available, but they depend on parametric assumptions. In this paper, nonparametric confidence cones based on the bootstrap are considered, and consistency of these methods is obtained under minimal conditions. We compare the performances of bootstrap methods with the others by Monte Carlo simulations.