1) Multi-assets Variance Analysis
多资产方差分解
2) variance decomposition
方差分解
1.
Based on Granger Causal test and ADF test,a VAR model was developed and the impulse response function and variance decomposition method were employed for quantitative study of the relationships between the industrial output,industrial wastewater effluent,and technical efficiency of industrial water utilization.
工业化进程导致的工业废水排放量与工业用水技术效率有关,为了量化研究工业产值、工业废水排放量与工业用水技术效率三者之间的关系,对数据检验之后,建立VAR模型,运用脉冲响应函数和方差分解方法进行分析。
2.
On the base of the model,it analyzes the dynamic effects of the relationship between the highway and economic development by the pulse response function and the variance decomposition.
在模型的基础上,运用脉冲响应函数和方差分解分析了高速公路与经济发展之间的动态效应。
3.
On the basis of the result unit root test, we established the VAR model and practice co integration test, and constituted the ECM and carry out Granger causality test, and analyzed series of hard wheat future and spot price by using variance decomposition and impulse responses function.
在此基础上先建立VAR模型并进行协整检验,然后建立误差修正模型并进行格兰杰因果检验,最后对期货与现货价格序列进行方差分解和脉冲响应函数分析。
3) variance decomposition proportion
方差分解比
1.
In this paper,a new method based on condition index and variance decomposition proportion(CIVDP) is proposed to diagnose the presence,number and composition of multicollinearities which exist among the columns of the design matrix in Gauss-Markov model for surveying adjustment.
运用条件指标和方差分解比提出一种诊断设计阵复共线性的新方法———条件指标-方差分解比(CIVDP)法,该方法不但可以确定设计阵数据列之间复共线性关系的个数,而且可以确定每个复共线性关系存在于哪些数据列之间,并通过大量的模拟计算和实际应用验证该方法的有效性。
4) Hasbrouck Variance Decompose
Hasbrouck方差分解
5) bias/variance decomposition
偏差/方差分解
1.
The selection of learning algorithms in meta-learning is investigated from the point of bias/variance decomposition as well as the effect of correlation on its accuracy.
提出一种元学习定义,从偏差/方差分解角度对元学习中学习算法的选取机制进行研究,得出了元级选用错误率低且偏差小的学习算法、基级学习算法按照错误率及方差从低到高排列的结论。
6) Variance decomposition analysis
方差分解分析
补充资料：国有非经营性资产转经营性资产
国有非经营性资产转经营性资产%26#8212;%26#8212;是指行政事业单位在保证完成正常工作的前提下，按照国家有关政策规定，将国有非经营性资产以出租、出借等形式从事生产经营活动。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条