We are discussing whether OLS estimator satisfy asymptotic normality.
我们讨论是否ols估计量满足渐近正态性。
The consistency and asymptotic normality behaviors are also investigated for the estimators.
我们也研究了估计的一致性和渐近正态性质。
The strong consistency, asymptotic normality and asymptotic efficiency of these methods are proved.
我们研究了这些方法的强相合性，渐近正态性和渐近有效性。
In this paper, we prove the strong consistency of the estimate, its efficiency asymptotic normality is discussed, too.
本文证明了这种估计的强相合性，并讨论了其优效渐近正态性。
In Chapter 4, we discuss and prove the consistency and asymptotic normality of maximum likelihood estimate to the exponential models.
第四章讨论了序贯指数模型的极大似然估计的强相合性和渐近正态性，并进行了证明。
This paper investigates the asymptotic normality of the estimation error of steady-state models of industrial processes in quite mild conditions.
本文研究了在相当弱的条件下工业过程稳态模型估计误差的渐近正态性。
It is proved that the statistics is asymptotic normality, and simulation of the statistics's asymptotic distribution is carried out with Monte Carlo method.
证明了此统计量是渐近正态的，并利用蒙特卡罗方法对统计量的渐进分布做了统计模拟。
Fan J and Gijbels I gave the asymptotic normality of local polynomial regression estimation in dependent time series, where the weighted function is bounded.
对相依时间序列数据，在一定的条件下已有人证明了局部多项式加权回归系数估计服从渐近正态分布，其中核函数是有界的。
In this paper, we use the convex function to form the order statistic and the linear rank statistic, and the asymptotic normality of the statistics are proved.
本文用凸函数构造了线性次序统计量和线性秩统计量，并证明了它们的渐近正态性。
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。
In this paper, empirical Euclidean likelihood ratio statistics are constructed for parametric in a nonlinear model. And prove strong consistency and asymptotic normality of the estimation.
本文构造了非线性模型中参数的经验欧氏似然比统计量，并证明了该似然估计的强相合性和渐近正态性。
The convergent property and convergent rate of parameter estimation error are analyzed . Some sufficient conditions are given to guarantee the asymptotic normality of parameter estimation error.
分析了离散时间线性系统模型参数估计误差的收敛性和收敛速度，对参数估计误差服从渐近正态分布的一些条件进行了讨论。
Given the asymptotic bias and the asymptotic variance of estimation, moreover obtained the asymptotic normality of the estimation under certain condition using small-block and large-block arguments.
给出非参数回归模型中估计量的渐近偏差和渐近方差，并在适当条件下利用大小分块的思想获得了该估计量的渐近正态性。
The weak consistence and the asymptotic normality of the robust M-estimate of the unknown function are given, and the weak consistence of the robust M-estimate of the unknown parameter is established.
进一步证明了未知函数的M-估计的弱一致性和渐近正态性，参数的M-估计的弱一致性。
The weak consistence and the asymptotic normality of the robust M-estimate of the unknown function are given, and the weak consistence of the robust M-estimate of the unknown parameter is established.
进一步证明了未知函数的M-估计的弱一致性和渐近正态性，参数的M-估计的弱一致性。
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