您要查找的是不是：
- DMSP/OLSDMSP/OLS
- OLSOLS (ordinary least squares analysis)
- OLS估計OLS estimator
- OLS回歸OLS regression
- DMSP／OLSDMSP/OLS
- OLS估計量OLS estimate
- OLS回歸斜率the slope of OLS
- 完全修正的OLSFMOLS
- 重疊保留（OLS）overlap-save (OLS)
- OLS性質(zhì)：最小化殘差平方和。Properties of OLS: minimize the sum of squared residuals.
- 正交最小二乘法（OLS）orthogonal least-squares(OLS)
- 我們討論是否OLS估計量滿(mǎn)足漸近正態(tài)性。We are discussing whether OLS estimator satisfy asymptotic normality.
- 如果這個(gè)較弱的假定也不成立，OLS將是有偏而且不一致的。Without this assumption, OLS will be biased and inconsistent!
- 基于OLS的徑向基函數神經(jīng)網(wǎng)絡(luò )實(shí)現多種數字信號調制方式自動(dòng)識別Automatic Digital Modulation Recognition Based on OLS Radial Basis Function Neural Network
- 由于OLS是用于最小化殘差平方和,當有變量被從模型中舍棄時(shí),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恰好使第二個(gè)解釋變量系數取零，那么不管回歸是否加入此解釋變量，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.
- 為了證明OLS估計量是漸近有效的，我們需要(1)給出一致的估計量但證明它有更大的方差。To prove that OLS estimators are asymptotically efficient, one needs to (1) present an estimator that is consistent but its variance is larger.
- 從在Sanaga流域上的應用表明,采用參數單位線(xiàn)的LPM能得到與采用非參數單位線(xiàn)(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.
- 由于在很多情形下誤差項可能呈現非正態(tài)分布，了解OLS估計量和檢驗統計量的漸近性，即當樣本容量任意大時(shí)的特性就是重要的問(wèn)題。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.
- 給出了平衡LS估計為無(wú)偏估計的充分必要條件,對于給定的L,適當地選擇參數t可使平衡LS估計的均方誤差矩陣小于OLS估計的均方誤差矩陣.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.
