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参数随机分散的线性回归AI 解读

Consider a collection of individual linear regression models, in which each individual parameter vector is independently drawn from a common multivariate normal distribution and is fixed over successive observations on that individual. Maximum likelihood estimators of the mean and dispersion of the parameters and of the disturbance variance are derived. These estimators yield empirical Bayes estimators of the individual parameter veotors. The properties of the estimators are…

参数随机分散的线性回归AI 解读

Consider a collection of individual linear regression models, in which each individual parameter vector is independently drawn from a common multivariate normal distribution and is fixed over successive observations on that individual. Maximum likelihood estimators of the mean and dispersion of the parameters and of the disturbance variance are derived. These estimators yield empirical Bayes estimators of the individual parameter veotors. The properties of the estimators are…

重新考察平稳随机回归参数的估计AI 解读

This article reexamines the problem of estimation in repeated regression experiments wherein the regression parameters vary according to a stationary stochastic process with known covariance structure. Best linear unbiased estimators, more efficient than those recently proposed by Burnett and Guthrie, are derived.

平稳随机回归参数估计的重新考察AI 解读

This article reexamines the problem of estimation in repeated regression experiments wherein the regression parameters vary according to a stationary stochastic process with known covariance structure. Best linear unbiased estimators, more efficient than those recently proposed by Burnett and Guthrie, are derived.

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