Rao and Zhao (1992) developed the random weighting method for M-estimates in regression models.
提出了一种用随机加权的方法去逼近线性回归模型中M-估计的渐近分布。
In this paper we find an uniformly random weighting method to statistics admitting asymptotic normality and study its consistency and convergence rates of uniformity and nonuniformity.
对一类具有渐近正态的统计量，找到了适用于它们的共同的随机加权逼近方法，并研究了它的相合性、一致及非一致逼近速度。
A reconstructing method for random weighting approximations is proposed in approach to the distributions of the parameter estimates in general linear regression model.
对一般线性回归模型中有关参数估计分布的模拟问题，给出一种随机加权逼近的再构造方法。
By applying an emergent random weighting estimation method to multi-sensor data fusion, a random weighting data fusion method of multi-source information disposing was proposed in this paper.
将一种新兴的随机加权估计方法应用于多传感器数据融合，提出了一种将多源信息综合处理的随机加权数据融合方法。
By applying an emergent random weighting estimation method to multi-sensor data fusion, a random weighting data fusion method of multi-source information disposing was proposed in this paper.
将一种新兴的随机加权估计方法应用于多传感器数据融合，提出了一种将多源信息综合处理的随机加权数据融合方法。
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