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位置参数的折中最大似然估计量AI 解读

This article describes a new approach for constructing robust location estimators. These estimators are constructed to be nearly optimal in small sample sizes simultaneously for two or more possible underlying shapes of the density of the data. The estimators are weighted averages of the maximum likelihood estimators for the densities considered, where the weights depend on the sample through the likelihood functions. Because of this relationship to the usual MLE's, these…

位置参数的折中最大似然估计AI 解读

This article describes a new approach for constructing robust location estimators. These estimators are constructed to be nearly optimal in small sample sizes simultaneously for two or more possible underlying shapes of the density of the data. The estimators are weighted averages of the maximum likelihood estimators for the densities considered, where the weights depend on the sample through the likelihood functions. Because of this relationship to the usual MLE's, these…

分位数—分位数图的多元推广AI 解读

In this article we present a multivariate generalization of quantile-quantile (Q-Q) plots. Like univariate Q-Q plots, these plots are useful for examining the distributional shape of multivariate point clouds. These plots are based on finding a matching between the points of the data set whose shape is being examined and a reference sample. Graphical displays of how well the point clouds match are then developed. The reference sample used as the basis for comparison is…

分位数—分位数图的多元推广AI 解读

In this article we present a multivariate generalization of quantile-quantile (Q-Q) plots. Like univariate Q-Q plots, these plots are useful for examining the distributional shape of multivariate point clouds. These plots are based on finding a matching between the points of the data set whose shape is being examined and a reference sample. Graphical displays of how well the point clouds match are then developed. The reference sample used as the basis for comparison is…

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