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1)  Moment estimation
矩型估计量
2)  moment estimator
矩估计量
1.
This article extends the moment estimator of the extreme-value index and proposes an extended moment estimator asIt s strong and weak consistency is proved and asymptotical distribution is derived.
本文对极值指数的矩估计量给以推广,提出一种新的估计量——推广的矩估计量: γ_(k,n)=M_n~(1)+1-(r-1)[r(1-(M_n~(1)M_n~(r-1))/(M_N~(r))]~(-1)主要证明了该估计量的相合性,得到了其渐近分布。
2.
Strong consistency and asymptotic normality of the moment estimator were proved.
利用矩方法和Bayes方法研究INGARCH(1,1)模型的参数估计问题,证明了矩估计量的强相合性和渐近正态性。
3)  moment-type estimator
矩型估计
1.
Based on the properties of mixed moments for (LnX1,LnX2), this article proposes two moment-type estimators 1 and 2 of 5, proves that both 1 and 2 have strong consistency and asymptotic normality, obtains asymptotic variances and of and respectively, and compares with .
根据(Lnx1,LnX2)的混合矩的性质,本文提出了δ的两个矩型估计δ1和δ2,证明了δ1和δ2都有强相合性和渐近正态性,得到了δ1和δ2的渐近方差σδ12和σδ22并把σδ12和σδ22作了比较。
4)  inverse moment estimation
逆矩估计量
5)  extended moment estimator
推广的矩估计量
1.
This article extends the moment estimator of the extreme-value index and proposes an extended moment estimator asIt s strong and weak consistency is proved and asymptotical distribution is derived.
本文对极值指数的矩估计量给以推广,提出一种新的估计量——推广的矩估计量: γ_(k,n)=M_n~(1)+1-(r-1)[r(1-(M_n~(1)M_n~(r-1))/(M_N~(r))]~(-1)主要证明了该估计量的相合性,得到了其渐近分布。
6)  traffic matrix estimation
流量矩阵估计
1.
Generalized regression neural network-based traffic matrix estimation
基于广义回归神经网络的流量矩阵估计
2.
With the exponential increase in the size of the IP network and the urgent demands in network management and maintenance, traffic matrix estimation has currently become an interesting topic.
随着IP网络规模指数式增长而带来的对网络管理和维护的迫切需求,流量矩阵估计已成为当前的热点研究问题。
3.
Finally, various factors is proposed, which should be considered carefully in improving the accuracy of traffic matrix estimation.
文中对现有的流量矩阵估计中几个最重要估计方法进行了分析和总结,指出它们的优点和缺陷,最后提出进一步估计流量矩阵所需要考虑的因素。
补充资料:量反应型量效关系
分子式:
CAS号:

性质:药量与效应间的关系。又称量效关系。如果效应强度是可测量的数据如血压、心率、细胞数、酶活性等,则称为量反应型量效关系。

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