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线性模型中的协方差矩阵估计AI 解读

In regression analysis with heteroscedastic and/or correlated errors, the usual assumption is that the covariance matrix σ of the errors is completely specified, except perhaps for a scalar multiplier. This condition is relaxed in this paper by assuming only that σ has a certain pattern; for example, that σ is diagonal or partitionable into a diagonal matrix of sub-matrices. The method used for estimating σ is the standard procedure of equating certain quadratic forms of the…

线性模型中的协方差矩阵估计AI 解读

In regression analysis with heteroscedastic and/or correlated errors, the usual assumption is that the covariance matrix σ of the errors is completely specified, except perhaps for a scalar multiplier. This condition is relaxed in this paper by assuming only that σ has a certain pattern; for example, that σ is diagonal or partitionable into a diagonal matrix of sub-matrices. The method used for estimating σ is the standard procedure of equating certain quadratic forms of the…

多元正态分布的置信域、预测域与容许域AI 解读

Formulas for confidence, prediction, and tolerance regions for the multivariate normal distribution for the various cases of known and unknown mean vector and covariance matrix are assembled for easy reference in this expository paper. Tables are provided for the bivariate case.

多元正态分布的置信区域、预测区域与容忍区域AI 解读

Formulas for confidence, prediction, and tolerance regions for the multivariate normal distribution for the various cases of known and unknown mean vector and covariance matrix are assembled for easy reference in this expository paper. Tables are provided for the bivariate case.