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- DMSP/OLSDMSP/OLS
- OLSOLS (ordinary least squares analysis)
- OLS估计OLS estimator
- OLS回归OLS regression
- DMSP／OLSDMSP/OLS
- OLS估计量OLS estimate
- OLS回归斜率the slope of OLS
- 完全修正的OLSFMOLS
- 重叠保留（OLS）overlap-save (OLS)
- OLS性质：最小化残差平方和。Properties of OLS: minimize the sum of squared residuals.
- 正交最小二乘法（OLS）orthogonal least-squares(OLS)
- 我们讨论是否OLS估计量满足渐近正态性。We are discussing whether OLS estimator satisfy asymptotic normality.
- 如果这个较弱的假定也不成立，OLS将是有偏而且不一致的。Without this assumption, OLS will be biased and inconsistent!
- 基于OLS的径向基函数神经网络实现多种数字信号调制方式自动识别Automatic Digital Modulation Recognition Based on OLS Radial Basis Function Neural Network
- 由于OLS是用于最小化残差平方和,当有变量被从模型中舍弃时,SSR必定上升.Idea: because the OLS estimates are chosen to minimize the sum of squared residuals, the SSR always increases when variables are dropped from the model.
- 如果OLS恰好使第二个解释变量系数取零，那么不管回归是否加入此解释变量，SSR相同。If OLS happens to choose the coefficient on the new regressor to be exactly zero, then SSR will be the same whether or not the second variable is included in the regression.
- 为了证明OLS估计量是渐近有效的，我们需要(1)给出一致的估计量但证明它有更大的方差。To prove that OLS estimators are asymptotically efficient, one needs to (1) present an estimator that is consistent but its variance is larger.
- 从在Sanaga流域上的应用表明,采用参数单位线的LPM能得到与采用非参数单位线(OLS)的LPM差不多的精度。The parametric LPM and the original LPM forms are applied on the sanaga catchment, Results, show that the parametric LPM forms can produce almost the same efficiency as the original LPM form.
- 由于在很多情形下误差项可能呈现非正态分布，了解OLS估计量和检验统计量的渐近性，即当样本容量任意大时的特性就是重要的问题。Since in many situations the error term is not normally distributed, it is important to know the asymptotic properties (large sample properties), i.e., the properties of OLS estimator and test statistics when the sample size grows without bound.
- 给出了平衡LS估计为无偏估计的充分必要条件,对于给定的L,适当地选择参数t可使平衡LS估计的均方误差矩阵小于OLS估计的均方误差矩阵.A necessary and sufficient condition for the unbiasedness of Balanced LS Estimation is gained, and for the given L, t can be chosen to make the MSEM of the Balanced LS Estimation less than that of OLS Estimation.
