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利用不完整数据估计多元正态密度AI 解读

This paper compares the estimative and predictive methods of estimating a multivariate normal density, and shows that in terms of a goodness-of-fit criterion based on an information measure, the predictive method is to be preferred for small sample sizes. We then extend the predictive method to certain nested patterns of missing data, and consider various approximations to the predictive method for more general patterns of missing data.

利用不完整数据估计多元正态密度函数AI 解读

This paper compares the estimative and predictive methods of estimating a multivariate normal density, and shows that in terms of a goodness-of-fit criterion based on an information measure, the predictive method is to be preferred for small sample sizes. We then extend the predictive method to certain nested patterns of missing data, and consider various approximations to the predictive method for more general patterns of missing data.

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