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利用一阶差分估计非参数回归方差的最优设计AI 解读

In nonparametric regression, the variance of the response can be estimated by the sum of squares of differences of the observed response. In this paper we obtain the most efficient design for a general variance estimator defined by first order differences. It is found that for this estimator, in the majority of cases, a good approximation to the most efficient design is the uniform design. The least efficient designs are also discussed.

利用一阶差分估计非参数回归方差的最优设计AI 解读

In nonparametric regression, the variance of the response can be estimated by the sum of squares of differences of the observed response. In this paper we obtain the most efficient design for a general variance estimator defined by first order differences. It is found that for this estimator, in the majority of cases, a good approximation to the most efficient design is the uniform design. The least efficient designs are also discussed.