论文标题
经验MSE最小化以估计标量参数
Empirical MSE Minimization to Estimate a Scalar Parameter
论文作者
论文摘要
当有两个估计器可用时，我们考虑标量参数的估计。第一个总是一致的。第二个通常是不一致的，但渐近方差比第一个差异较小，如果满足假设，则可能是一致的。我们建议使用具有最低估计于点误差（MSE）的两个估计量的加权总和。我们表明，第三个估计量从最小值 - 重新格局的角度统治了其他两个：最大渐近生长生成可能会通过使用该估计器而不是其他估计量来产生的最大估计值大于最大渐近线损坏。
We consider the estimation of a scalar parameter, when two estimators are available. The first is always consistent. The second is inconsistent in general, but has a smaller asymptotic variance than the first, and may be consistent if an assumption is satisfied. We propose to use the weighted sum of the two estimators with the lowest estimated mean-squared error (MSE). We show that this third estimator dominates the other two from a minimax-regret perspective: the maximum asymptotic-MSE-gain one may incur by using this estimator rather than one of the other estimators is larger than the maximum asymptotic-MSE-loss.
