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含缺失值多元回归的极大似然推断：模拟研究AI 解读

Summary Maximum likelihood inference for the coefficients of a multiple regression with missing values is studied by simulation, using artificially generated multivariate normal observations with randomly deleted missing values. The method is compared with least squares on the complete observations by mean squared error. Two methods of estimating the covariance matrix of the m.l. estimates are compared, and the applicability of large sample theory to obtain tests and…

不完整数据判别分析的一致回归方法AI 解读

Modifications are proposed for a method of discriminant analysis with missing values described by Chan, Gilman, and Dunn (1976). The modified methods are special cases of techniques for handling missing values in multivariate linear regression. Less stringent conditions for when missing values are missing at random are suggested.