asymptotic normality
英
美
渐近正态性
双语例句
- 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、致及非一致逼近速度。 - Asymptotic normality of maximum quasi-likelihood estimators in multivariate generalized linear models
多维广义线性模型极大拟似然估计的渐近正态性 - The consistency and asymptotic normality of the estimations have been proved. Moreover, statistic on testing equality of two exponential distributions and its limit distribution are given.
证明了估计的强相合性以及渐近正态性，给出了检验两总体参数相等的检验统计量及检验统计量的极限分布。 - Asymptotic Normality of Sample Correlation Coefficient of a Bivariate Normal Distribution
二元正态总体样本相关系数的渐近正态性 - Under relatively simple conditions, the consistency and the asymptotic normality of the conditional ML estimator for stochastic regressors case are obtained.
最后，在相对简单的正则条件下，证明了随机回归变量条件下ML估计的相合性和渐近正态性。 - Asymptotic Normality of Wavelet Estimator of Regression Function under α-mixing Dependent Errors
强混合误差下回归函数小波估计的渐近正态性 - 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。 - Asymptotic normality of partitioning estimates for the nonparametric regression function under censored samples
截尾样本下非参数回归函数基于分割估计的渐近正态性 - The linear EV model with replicated observations only on explanatory variables is studied. Estimators of parameters are given. The consistency and asymptotic normality of the given estimators are proved with the help of extension of Jamison Theorem.
研究了一类仅允许自变量可进行重复观测的线性EV模型，给出了参数的估计，通过推广Jamison的定理证明了估计量的相合性和渐近正态性。 - In this paper, asymptotic normality of the LS estimation of the two-dimensional AR process is given.
本文给出了两指标AR过程LS估计的渐近正态性。 - 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。 - As a result, the asymptotic normality of σ 2 is obtained under some suitable conditions.
综合最近邻法和最小二乘法，定义了β、g和σ2的估计量，在适当的条件下证明了σ2的估计量的渐进正态性。 - In this paper, we introduce the recursive double kernel estimators of conditional density, and prove the asymptotic normality of the estimators under much weaker conditions. We have also weakened the Theorem's conditions in the [ 1] and obtain as results as the Theorem.
本文引进了条件密度递归形式的双重核估计，并在适当的条件下证明了这种估计满足渐近正态性，本文还削减了文献[1]中定理的条件，其结果和该定理是一样的。 - In this paper, we discuss the uniformly asymptotic normality of the nonparametric regression weighted estimator and give the rates of the uniformly asymptotic normality for stationary and positively associated samples.
在平稳PA样本下，讨论了非参数回归模型中权函数估计的一致渐近正态性，并给出了这个估计的一致渐近正态性的收敛速度。 - The rate of weak uniform convergence of the PL type estimator over the whole line is given and asymptotic normality of the estimator is proved by the approximation of empirical process and Taylor expansion.
运用经验过程的逼近理论及Taylor展开方法，给出了估计在全直线上的弱一致收敛速度，并证明了估计的渐近正态性。 - The asymptotic normality of parametric estimate, the optimal convergence rate of the nonparametric;
证明了参数分量的渐近正态性，得到了非参数分量的最优收敛速度； - Under second regularly varying condition, the asymptotic normality of those index estimators was derived.
在二阶正规变化条件下，得到了此类估计量的渐近正态性。 - A Result on Asymptotic Normality for Time Series Sum
时间序列和渐近正态性的一个结果 - Masry ( 1986,1987) discussed both quadratic-mean consistency and asymptotic normality for dependent sample and asymptotically uncorrelated;
在相依样本情形，Masry（[3]，[4]19861987）分别对ρ-混合和渐近不相关情形讨论fn（x）的均方收敛和渐近正态性； - 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.
2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。 - Strong consistency and asymptotic normality are discussed under the general conditions of design points and moments.
并在一般设计点列和一般矩条件下，建立了估计量的强相合性和渐近正态性。 - In chapter two we investigated the consistency and asymptotic normality property of NN-estimator under α-mixing condition.
第二章介绍了α混合序列的概念，讨论了在α混合情形下最近邻密度估计的强、弱相合性及渐近正态性。 - This paper presents maximum weighted likelihood estimation of parametric regression models and proves its asymptotic properties such as consistency and asymptotic normality by using laws of large numbers and central limit theory.
提出参数回归模型的最大加权似然估计方法并利用概率论中的大数定律和中心极限定理证明了估计的一致性和渐近正态性。 - The consistency and asymptotic normality of the method are proved.
我们证明了矩估计具有强相合性和渐近正态性。 - By the use of convergence property of sums of NA sequences, the o ( n~-1/ 4) convergence rate of parametric part and the strong consistency of nonparametric part are proved. We also obtain the asymptotic normality of parametric part.
利用NA序列和式的收敛性质，得到了参数估计的o（n~-1/4）收敛速度和非参数部分的强相合性，并进一步证明了参数部分估计的渐近正态性。 - In theory, we investigate the divergent rate for the dimension of predictors when consistency and asymptotic normality hold.
在理论上，我们考察自变量维数发散的情形，研究了估计相合性与渐近正态性成立时发散维数的发散速度。 - The asymptotic normality of maximum quasi-likelihood estimators ( MQLEs) in generalized linear models with natural link function and adaptive designs is discussed.
本文研究了在自适应设计和自联系情况下，广义线性模型极大拟似然的渐近正态性性。 - The asymptotic normality of parametric part and the strong consistency of nonparametric part are proved under some weak conditions. 4.
在较弱的假设条件下证明了参数部分的渐近正态性和非参数部分的强相合性。 - The thesis also studies the consistency and asymptotic normality of the estimators. We give their proof in the appendix.
本文还研究了参数的相合性和渐进正态性，并在附录中给出了证明。 - Thirdly, the asymptotic normality of the resulting estimators is established.
第三，给出了相应估计的渐近性质。