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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.