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Estimation of the mean of a univariate normal distribution when the variance is not known

机译:当方差未知时估计单变量正态分布的均值

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摘要

We consider the problem of estimating the first k coefficients in a regression equation with k + 1 variables. For this problem with known variance of innovations, the neutral Laplace weighted-average least-squares estimator was introduced in Magnus (2002). We generalize this estimator to the case where the unknown variance is estimated by least squares and find that main properties of the Laplace estimator only change marginally. Therefore we recommend the neutral Laplace estimator to be used in practice.
机译:我们考虑用k + 1变量估计回归方程中前k个系数的问题。对于具有已知创新差异的此问题,Magnus(2002)引入了中性拉普拉斯加权平均最小二乘估计器。我们将该估计器推广到最小方差估计未知方差的情况,并发现拉普拉斯估计器的主要属性仅发生少量变化。因此,我们建议在实践中使用中性拉普拉斯估计量。

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