概率分布函数序列弱收敛的一个充分条件

概率分布函数序列弱收敛的一个充分条件陈俊雅（天津师范大学数学系３０００７４）设Ｆ，Ｆ＿１，Ｆ＿２，…均为概率分布函数，它们的ｋ阶矩分别为文献［１］中给出了下述结果：如果上述每个概率分布函数的各阶矩均有限，且满足：１）本文改进了这一充分条件，得到了下面...     

一种近邻密度估计的强收敛速度

本文讨论了俞军（１９８６）提出的一种近邻密度估计的逐点强收敛速度和一致强收敛速度．并证明了收敛速度的主阶部分不能达到．     

非参数回归函数核估计的强收敛速度

本文给出回归函数m(x)=E(Y|X=x)满足λ(0<λ≤1)阶Lipschitz条件,且E|Y|~r<∞,r>1时,对m(x)的核估计有同时本文也改善了赵林城、方兆本(1985年)和孙东初(1985年)关于m_n(x)强相合于m(x)的结果。     

相依误差下回归函数导数估计的强收敛速度

设Y_1,…,Y_n是在固定点x_1,…,x_n的n个观察值,适合模型 Y_i=g(x_i) ε_i,1≤i≤n.(1)这里g(·)是R上的未知函数,{ε_i}为随机(误差)变量序列,且假定0=x_0≤x_1≤…≤x_(n-1)≤x_n=1. 给定非负整数p,为了估计g的p阶导数g~(p)(x)(p=0时,即为g(x)),秦永松用     

截尾数据非参数回归函数加权核估计的强相合性

本文对截尾数据其随机误差分别为独立与ψ－混合两种情形得到了非参数回归函数加权核估计的强相合性。     

线性模型中回归分位数估计的强相合性

考虑线性系统模型 y=x＇β_0 x＇r_0e, (1)其中x为P维已知向量,β_0是未知的P维回归系数,e是1维不可观察的误差随机变量。 y在x下的条件u分位数可以写成:     

截断数据下非参数回归函数估计的强相合性

本文给出了截断数据下非参数回归函数ｍ（ｘ）＝Ｅ（Ｙ｜Ｘ＝ｘ）的两种估计。在一定的条件下证明了第一种估计的强相合性且给出了第二种估计的强收敛速度。     

分位点过程随机加权逼近的相合性

本文将随机加法应用于分位点过程，建立了ｎ＾１／２｛Ｆ＾－Ｄ１ｎ（ｇ）－Ｆ＾－１（Ｇ）｝的分布的随机加权逼近的相合性，并给出了其收敛速度。     

完全与截尾样本时回归函数的核估计

本文得到了完全与截尾样本时回归函数核估计的强相合性．接着，构造了截尾样本的改良核估计，在Ｅ｜Ｙ｜＜∞下，得到了其强相合性．     

非参数回归函数之改良基于分割的估计的强相合性（英）

设(X,Y),(X1,Y1),…,(XnYn)为取值于 Rd× R的 i．i．d．随机变量,E(|Y|) ＜∞．设mn(x)为回归函数m(x)=E(|Y|X=x)基于分割的估计,本文在对mn(x)进行改良的条件下得到改良的基于分割的强相合估计．     

基于负相协样本经验过程的加权弱收敛

在文中,我们建立了基于负相协样本经验过程的加权弱收敛,同时利用这个结果,我们还证明了其积分的弱收敛性,并将这些结果应用于生存分析,获得屯一些相应的弱极限性质。     

α混合下非参数回归函数改良分割估计的强相合性及收敛速度

In this paper, we study the strong consistency and convergence rate of modified partitioning estimate of nonparametric regression function under the sample {(Xi, Yi),i ≥ 1} that is α sequence taking values in Rd × R1 with identical distribution.     

$\alpha$混合下非参数回归函数改良分割估计的强相合性及收敛速度

本文研究了在样本$(X_1,Y_1),(X_2,Y_2),\ldots,(X_n,Y_n)$ 为取值于$R^{d}\times R^{1}$的同分布的$\alpha$混合序列时,回归函数改良分割估计的强相合性和收敛速度.     

基于负相协样本的经验过程的弱收敛

我们借助于一个Ｒｏｓｅｒｎｔｈａｌ型不等式建立了基于负相协样本经验过程的弱收敛性,同时在证明过程中我们给出了负相协随机变量的几个有用的矩不等式。     

NA样本非参数回归权函数估计的强相合性

在 NA样本下 ,讨论了非参数回归模型中权函数估计的强相合性及强一致相合性 ,并把这个结果应用于 Gasser- Muller估计和 Priestley and Chao估计 .     

Universal Consistency of Local Polynomial Kernel Regression Estimates

Regression function estimation from independent and identically distributed data is considered. The L ₂ error with integration with respect to the design measure is used as an error criterion. It is shown that suitably defined local polynomial kernel estimates are weakly and strongly universally consistent, i.e., it is shown that the L ₂ errors of these estimates converge to zero almost surely and in L ₁ for all distributions.     

相依样本下非参数回归函数基于分割估计的强相合性

In this paper, we study the strong consistency for partitioning estimation of regression function under samples that axe φ-mixing sequences with identically distribution.Key words: nonparametric regression function; partitioning estimation; strong convergence;φ-mixing sequences.     

On the Approximation of Quantile Processes by Kiefer Processes

We prove that the best possible almost sure rate of uniform approximation of a uniform quantile process by a normed Kiefer process is O(n ^–1/4(log n)^1/2× (log log n)^1/4).     

On Weak and Strong Convergence of the Projected Gradient Method for Convex Optimization in Real Hilbert Spaces

This work focuses on convergence analysis of the projected gradient method for solving constrained convex minimization problems in Hilbert spaces. We show that the sequence of points generated by the method employing the Armijo line search converges weakly to a solution of the considered convex optimization problem. Weak convergence is established by assuming convexity and Gateaux differentiability of the objective function, whose Gateaux derivative is supposed to be uniformly continuous on bounded sets. Furthermore, we propose some modifications in the classical projected gradient method in order to obtain strong convergence. The new variant has the following desirable properties: the sequence of generated points is entirely contained in a ball with diameter equal to the distance between the initial point and the solution set, and the whole sequence converges strongly to the solution of the problem that lies closest to the initial iterate. Convergence analysis of both methods is presented without Lipschitz continuity assumption.