Mean-square continuity

After the concept of mean square continuous module is built and its relevant character is obtained,the approximate order of random function approximated by trigonometric polynomial of random coefficient is researched.

建立了随机函数均方连续模的概念,并得到其有关性质后,研究了四种不同条件下随机函数被随机系数三角多项式逼近的阶之估计。

In this paper,the continuity of Hilbert space value random function is discussed, and the relationship of continuity in the mean and continuity in the random is established.

本文对Ｈｉｌｂｅｒｔ空间值随机函数的连续性作了探讨，建立了均方连续性与随机连续性及其弱连续性的关

Firstly,screen the descriptors using support vector machine regression(SVR) by leave-one-out method based on the minimum mean square error(MSE),get the optimal kernel and the corresponding retained descriptors.

首先以均方误差(MSE)最小为原则,以留一法通过多轮末尾淘汰实施分子结构描述符的非线性SVR汰选并给出最优核函数和相应保留描述符;其次基于待测样本与训练样本保留描述符向量的欧氏距离,以不同k-近邻群子模型双重留一法预测值反映样本集的异质性;然后基于MSE最小,以留一法通过多轮末尾淘汰实施近邻群子模型的非线性SVR汰选并给出最优核函数和相应保留子模型;最后基于保留子模型以双重留一法实施组合预测。

For the generalized linear model with aggregated data:Y=Xβ+u,Eu=0,Var(u)=σ2∑,this paper is built two kinds of biased estimators: ridge estimator β(k)and improved ridge estimator β(k)which are discussed some superiority to the estimators in the sense of mean square error.

在均方误差意义下,研究了它们的优良性,并将岭估计与改进岭估计进行了比较,推广了有关文献中的结果。

As to seemingly unrelated regression system,a new biased contracting estimator of the parameters is put forward,which is the combination of generalized ridge covariance-improved estimator and Stein estimator,and the good features of this estimator in mean square error is discussed.

对于一类相依回归系统,结合广义岭型协方差改进估计与Ste in估计,提出了一种新的有偏压缩估计,,并讨论了该估计在均方误差下的优良性质。

The criterion is that the location with according to the maximal mean square deviation of the AE signals is the height level of fluidized bed.

利用颗粒运动碰撞壁面产生声波的机理以及声波信号随流化床高的规律性变化,根据料位处的声信号能量及声信号均方差均达到最大的状态,提出了声信号测定料位高度的判据,即声信号的均方差比达到最大时的床高即为料位高度。

The reason is that the formula of calculating mean square deviation .

研究发现其根本原因是回归分析中均方差计算公式不合适。

A linear relation is proved to exist between the average gas consumption and mean square deviation.

本文通过对运输车队每天油耗数据的收集整理 ,采用回归分析方法 ,得出运输车队平均油耗与均方差之间存在显著的线性关系 ,从而可考虑通过缩小一个车队油耗的均方差来降低平均油耗 ,达到节约用油提高经济效益的目的 。