ψ—混合样本核密度估计的强一致相合性

本文研究了基于φ-混合样本且核为可变化的密度函数估计的强一致相合性,获得了与独立同分布样本时同样优良的收敛速度O((n/logn)~(-1/3)).     

刻度指数族参数的经验Bayes检验函数收敛速度的改进

在加权线性损失下导出了刻度指数族中参数单调的Bayes检验函数,利用同分布负相协(NA)样本情形概率密度函数及其导数的核估计构造了经验Bayes(EB)检验函数,获得了EB检验函数的收敛速度.在适当的条件下,这一收敛速度可任意接近O(n~(-1)),改进了文献中已有的结果.对同分布正相协(PA)样本和独立同分布(iid)样本情形,亦可获得类似结论.最后给出了一个满足文中主要结果的例子.     

相依样本的经验过程振动模的收敛性

文中研究了两类重要相依样本(即φ-混合和α-混合样本)的经验过程振动模强一致收敛速度,证明了该速度与独立样本下的经验过程振动模的最优收敛速度相同.利用这些结果建立了密度函数核估计和直方图核估计的强相合性,并证明了这些强相合收敛速度达到最好速度O(n~(-1/3) log~(1/3)n)以及建立分位估计Bahadur类型的表示定理.     

关于ρ-混合序列对数律的收敛速度

本文研究了ρ-混合序列对数律的收敛速度，在较弱的矩条件下得到了与独立同分布实随机变量类似的结果，并获得了ρ－混合序列满意对数律的一个充分性结果；讨论了ρ－混合序列重对数律的收敛速度的问题，得到了一个重对数律的充分性条件。     

相依样本下污染线性模型的最近邻估计

考虑一般线性模型,设误差序列{ei}是平稳的α-混合序列,具有公共未知密度,f(x)．本文首先讨论了基于残差的f(x)的最近邻估计的相合性及收敛速度,然后把结论推广到污染线性模型,讨论了污染系数ε,误差的主体分布及回归系数β的估计的相合性,收敛速度以及(β|^)的渐近正态性．     

(p)混合序列的矩不等式与完全收敛性

给出一类较广泛的(p)混合序列的矩不等式.讨论了(p)混合序列的完全收敛性,所得的结果改进了相关文献中的结果,并得到了完全收敛速度与矩条件之间的等价关系.     

无界混合序列强律的收敛速度

本文进一步研究无界相依随机变量序列部分和的Marcinkiewicz-Zygmund强律的收敛速度,在对随机变量的矩给出一定的限制时,关于无界ψ-混合序列(未必平稳)得到了与[2]中主要结果相类似的结论。     

基于α-混合序列的学习机器一致收敛速率的界

Vapnik,Cucker和Smale已经证明了,当样本的数目趋于无限时,基于独立同分布序列学习机器的经验风险会一致收敛到它的期望风险.本文把这些基于独立同分布序列的结果推广到了α-混合序列,应用Markov不等式得到了基于α-混合序列的学习机器一致收敛速率的界.     

相依样本分布函数和回归函数核估计的强收敛性及其速度

本文讨论样本为φ-混合和α-混合时分布函数核估计的强相合性.在α-混合时讨论其收敛速度,我们的结果与i.i.d.情况相一致,从而改进了[2]中的结论。同时,本文还在ρ-混合下,讨论回归函数核估计的强收敛性及收敛速度,其结果接近于独立情形。     

基于$\alpha$\,-混合序列的学习机器一致收敛速率的界

Vapnik, Cucker和Smale已经证明了, 当样本的数目趋于无限时, 基于独立同分布序列学习机器的经验 风险会一致收敛到它的期望风险\bd 本文把这些基于独立同分布序列的结果推广到了$\alpha$\,-混合序列, 应用Markov不等式得到了基于$\alpha$\,-混合序列的学习机器一致收敛速率的界     

Regularized least square regression with dependent samples

In this paper we study the learning performance of regularized least square regression with α-mixing and ϕ-mixing inputs. The capacity independent error bounds and learning rates are derived by means of an integral operator technique. Even for independent samples our learning rates improve those in the literature. The results are sharp in the sense that when the mixing conditions are strong enough the rates are shown to be close to or the same as those for learning with independent samples. They also reveal interesting phenomena of learning with dependent samples: (i) dependent samples contain less information and lead to worse error bounds than independent samples; (ii) the influence of the dependence between samples to the learning process decreases as the smoothness of the target function increases.     

渐近负相关随机域强定律的收敛率

In this paper,a notion of negative side ρ-mixing (ρ--mixing) which can be regarded as asymptotic negative association is defined,and some Rosenthal type inequalities for ρ--mixing random fields are established. The complete convergence and almost sure summability on the convergence rates with respect to the strong law of large numbers are also discussed for ρ--mixing random fields. The results obtained extend those for negatively associated sequences and ρ*-mixing random fields.     

两两独立序列加权和的L~r收敛性

讨论了两两独立随机变量列加权和在满足r(1≤r<2)阶Ces`aro一致可积条件下的Lr收敛性,获得了与独立情形一致的结果.用相似的方法,对于其它相依或混合序列(如两两NQD列,φ-混合序列,ρ-混合序列)也有相同的结果.     

Convergence rates in the strong laws of asymptotically negatively associated random fields

In this paper, a notion of negative side p-mixing (p -mixing) which can be regardedas asymptotic negative association is defined, and some Rosenthal type inequalities for p -mix-ing random fields are established. The complete convergence and almost sure summability onthe convergence rates with respect to the strong law of large numbers are also discussed for p--mixing random fields. The results obtained extend those for negatively associated sequences andp“ -mixing random fields.     

Sieve Estimates via Neural Network for Strong Mixing Processes

In this paper, we obtain the convergence rates of sieve estimates for α-mixing strictly stationary processes in the special case of neural networks. When the entropy of sieve satisfies certain conditions, we establish the bounds of sieve estimate. Using the bounds we give finally convergence rate of sieve estimate via neural networks. This revised version was published online in June 2006 with corrections to the Cover Date.     

Almost sure convergence of the kT-occupation time density estimator

In this Note, we introduce an extension of the k-nearest neighbor estimator in continuous time, the $k_T$-occupation time estimator, and we give sufficient conditions for its existence. Then, we show the almost sure convergence for α-mixing and bounded processes in two cases, the superoptimal case (when parametric rates are reached) and the optimal case (when i.i.d. rates of density estimation are reached). To cite this article: B. Labrador, C. R. Acad. Sci. Paris, Ser. I 343 (2006).     

Moment Complete Convergence for Weighted Sums of -Mixing Random Variables

In this paper, the authors discuss the moment complete convergence for weighted sums of -mixing random variables, and obtains the sufficient condition for moment complete convergence of -mixing sequence under some mixing rate condition, which generalize the result of moment complete convergence for weighted sums of i.i.d. random variables to -mixing random variables.     

行为ρ混合随机变量阵列的完全矩收敛性

本文假设 X ni ;i ≥1,n ≥{}1是一列行为？ρ混合随机变量阵列。在没有同分布和随机控制的假设条件下,作者讨论了？ρ混合随机变量的完全矩收敛性,所获得结果推广和改进了 Hu and Taylor （1997）,Zhu （2006）和 Wu and Zhu （2010）的相应定理。