查询词典 estimators

Under assumptions MLR.1 through MLR.4, the OLS estimators are unbiased estimator of the population parameters, that is

在假定MLR.1～MLR.4下，OLS估计量是总体参数的无偏估计量，即

The adjusted R-square is still not an unbiased estimator of the population R-squared, because the ratio of two unbiased estimators is not an unbiased estimator.

调整过的R2仍不是总体R2的一个无偏估计量，因为两个无偏估计量的比例不是一个无偏估计量。

Under CLM, OLS is not only BLUE, but also the minimum variance unbiased estimator, that is, among linear and nonlinear estimators, OLS estimator gives the smallest variance.

在经典线性模型假设下，OLS不仅是BLUE，而且是最小方差无偏估计量，即在所有线性和非线性的估计量中，OLS估计量具有最小的方差。

Compared with the maximum likelihood estimation which is commonly used for type-I censored data in engineering, the presented method can reach a high precision by simple calculation, and the estimators show best unbiased property with small sample.

与目前工程中常用的极大似然估计(maximum likelihood estimation， MLE)方法相比，文中方法所得估计量不但在大样本而且在小样本情况下也具有最佳无偏性质，且计算简单、精度高，便于工程应用。

There is no effect on the unbiasedness of the OLS estimators.

过度设定对我们的参数估计没有影响，OLS仍然是无偏的。

We begin by establishing the unbiasedness of OLS estimators under a set of assumptions.

首先，我们在一些假定下证明OLS 估计量的无偏性。

Strongly consistency and asymptotically unbiasedness of new estimators are proved by means of integrating finite increment with infinitesimal one.

数值实验结果表明估计量的相对误差和无偏性检验值都比较小，新算法能够很好地估计M/G/1排队系统的性能梯度。

The uniformization embeded Markov chain is used to study the sensitivity of a continuos-time Markov process. It is proved that sensitivity estimators provided by the embeded Markov chain have less variance.

提出了用一致化嵌入马氏链来研究连续时间马氏过程的灵敏度分析方法，证明了用嵌入马氏链不仅实现简单而且还可以减小灵敏度估计量的方差。

Cost estimators also need knowledge of computers, including word-processing and spreadsheet packages.

成本估计也需要知识的电脑，包括文字处理和试算表的软件包。

Similarly, we give out the proof of the consistency and asymptotical normality of the estimators.

使用类似的手法，我们证明了该估计的相合性和渐近正态性。

Warden: That's a beautiful dog, but don't bring her again.

那是条漂亮的狗，但是不要再把她带来了。

You get up in the morning – no hangover!

你第二天早上起来的时候—不会宿醉！