一类统计量的强大数律和重对数律的精确极限性质

设{Xn,n≥1}是独立同分布随机变量序列,EX1=0,EX12=1.设Sn=n∑i=1Xi,Tn=Tn(X1,…,Xn)是随机函数且Tn=Sn+Rn.本文证明在E|Rn|2∨r《∞或E|Rn|《∞下,对随机函数Tn成立着Baum-Katz强大数律和重对数律的精确极限性质的一般结果.由此作为推论,对U-统计量,Von-Mises统计量,线性过程,移动平均过程,线性模型中误差方差估计和功率和等在适当矩条件下均可写出Baum-Katz强大数律和重对数律的精确极限性质.     

B值平稳线性过程的迭对数律及随机指标中心极限定理

设 { εt;t∈Z}是独立同分布的 B值随机元序列 ,aj;j∈ Z是一实数序列 ,并且 ∞j=-∞| aj| <∞ ,定义平稳线性过程 Xt= ∞j=-∞ajεt- j.本文研究 { Xt;t∈ IN }部分和序列的收敛性质和极限定理 ,给出了 { Xt;t∈ IN }满足有界迭对数律、紧迭对数律及随机指标中心极限定理的充分条件     

Almost sure central limit theorems for random functions

Let {Xn,-∞< n <∞} be a sequence of independent identically distributed random variables with EX1 = 0, EX12 = 1 and let Sn =∑k=1∞Xk, and Tn = Tn(X1,…,Xn) be a random function such that Tn = ASn Rn, where supn E|Rn| <∞and Rn = o(n~(1/2)) a.s., or Rn = O(n1/2-2γ) a.s., 0 <γ< 1/8. In this paper, we prove the almost sure central limit theorem (ASCLT) and the function-typed almost sure central limit theorem (FASCLT) for the random function Tn. As a consequence, it can be shown that ASCLT and FASCLT also hold for U-statistics, Von-Mises statistics, linear processes, moving average processes, error variance estimates in linear models, power sums, product-limit estimators of a continuous distribution, product-limit estimators of a quantile function, etc.     

NA序列重对数律的几个极限定理

设{X_n;n≥1}均值为零、方差有限的NA平稳序列。记S_n=∑_(k=1)~n X_k,M_n=maxk≤n|S_k|,n≥1.假设σ~2=EX_1~2+2∑_(k=2)~∞EX_1X_k>0。本文讨论了:当ε 0时,P{M_n≥εσ(2nloglogn)~(1/2)的一类加权级数的精确渐近性质,以及当ε∞时,P{M_n≤εσ(π~2n/(8loglogn))~(1/2)}的一类加权级数的精确渐近性质。这些性质与重对数律和Chung重对数律的速度有关。     

两两独立同分布序列Cesàro强大数定律的收敛速度

设{X,Xn,n≥0}是两两独立同分布的随机变量序列,11．为了证明这一结论而获得到的两两负相关随机变量序列的Cesaro强大数定律收敛速度的结果本身也是有意义的．此结果对于同分布的两两NQD序列也是对的．     

自正则化和Davis大数律和重对数律的精确渐近性

本文证明了自正则化Davis大数律和重对数律的精确渐近性, 即 {\heiti\bf 定理1}\hy 设$\ep X=0$, 且$\ep X^2I_{(|X|\leq x)}$在无穷远处是缓变函数, 则$\lim_{\varepsilon\searrow0}\varepsilon^2\tsm_{n\geq3}\frac{1}{n\log n}\pr\Big(\Big|\frac{S_n}{V_n}\Big|\geq\varepsilon\sqrt{\log\log n}\Big)=1.${\heiti\bf 定理2}\hy 设$\ep X=0$, 且$\ep X^2I_{(|X|\leq x)}$在无穷远处是缓变函数, 则对本文证明了目正则化Davis大数律和重对数律的精确渐近性,即定理1设EX=0,且EX~2I_(|x|≤x)在无穷远处是缓变函数,则■定理2设EX=0,且EX~2I_(|x|≤x)在无穷远处是缓变函数,则对0≤δ≤1,有■其中N为标准正态随机变量．     

关于最近邻回归估计的收敛速度

设(X,Y),(X_1,Y_1),(X_2,Y_2),…为取位于 R~d×R~1上的 iid.随机向量序列,E|y|<∞.本文研究了回归函数 m(x)的最近邻估计 m_n(x)的强收敛速度问题,在一定条件下证明了它满足重对数律,即■(|m_n(x)-m(x))/(2∑_i~k1v_(ni)~2log logn)~(1/2)≤(2var(Y|X=x))~(1/2)a.s.     

随机函数的几乎处处中心极限定理

设{X_n,n≥1}是独立同分布随机变量序列,EX_1=0,EX_1~2=1．设S_n=∑_i~n=1 X_i,T_N=T_N(X_1,…,X_n)是随机函数且T_N=AS_N+R_n．我们证明若supE|R_n|＜∞,R_n=o n~(1/2)a．s．或R_n=O(n~(1／2-2γ))a．s．(0＜γ＜1／8),则对随机函数T_n几乎处处中心极限定理(简记为ASCLT)和函数型几乎处处中心极限定理(简记为FASCLT)成立．由此作为推论,可得对U统计量、Von-Mises统计量、线性过程、移动平均过程、线性模型中误差方差估计、功率和、连续分布函数的乘积极限估计和分位点函数的乘积极限估计等均成立着ASCLT和FASCLT．     

OU过程无穷级数在L2—模下的重对数律与Chung—重对数律

设（Ｘｋ（ｔ），－∞＜ｔ＜∞）ｋ＝１为一列相互独立的Ｏｒｎｓｔｅｉｎ－Ｕｈｌｅｎｂｅｃｈ过程，（Ｘ（ｔ）＝∑Ｘｋ（ｔ），－∞＜ｔ＜∞）为其无穷级数，本文讨论了（Ｘ（ｔ），－∞＜ｔ＜∞）在Ｌ２模下的极限性质，得到了与Ｗｉｅｎｅｒ过程相似的重对数律与Ｃｈｕｎｇ－重对数律。     

I.I.D.随机变量序列矩完全收敛的精确渐近性

{X,Xn;n≥1}为独立同分布的随机变量序列, EX=0,01 p/2满足E|X|r<∞,且E|X|3<∞,那么其中Z服从均值为0,方差为σ2的正态分布.     

Some asymptotic results related to the law of iterated logarithm for Brownian motion

For 0<<1, let . The questions addressed in this paper are motivated by a result due to Strassen: almost surely, lim sup _t U ((t))=1–exp{–4(^–1)–1}. We show that Strassen's result is closely related to a large deviations principle for the family of random variablesU (t), t>0. Also, when =1,U (t)0 almost surely and we obtain some bounds on the rate of convergence. Finally, we prove an analogous limit theorem for discounted averages of the form as 0, whereD is a suitable discount function. These results also hold for symmetric random walks.     

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。     

快速造P(n,k)大表的左肩法则和斜线法则

设Ｐ（ｎ,ｋ）为整数ｎ分为ｋ部的无序分拆的个数,每个分部≥１,它为大师欧拉所建立（１７０７－１７８３）．它是组合图论和数论里最重要的数据之一．然而,它却十分难于计数和造表．本文,由公式Ｐ（ｎ,ｋ）＝Ｐ（ｎ－１,ｋ－１）＋Ｐ（ｎ－ｋ,ｋ）定义了Ｐ（ｎ,ｋ）的左肩数和锐角数,并由此得到求Ｐ（ｎ,ｋ）的左肩法则（第一法则）．还根据本文作者［５］的一些重要定理得到求 Ｐ（ｎ,ｋ）的斜线法则（第二法则）．使用这些法则得到造Ｐ（ｎ,ｋ）大表的有趣原理．为方便计,我们仅用第一法则设计了计算机程序,用此程序即可快速造出任意大的Ｐ（ｎ,ｋ）表．     

模糊数的四则运算性质及其线性方程

讨论模糊数的加、减、乘、除的运算性质,提出模糊数线性方程的概念,并给出这种方程的一种解法。     

B-值随机变量序列大数定律的收敛速度

设{X_n:n≥1}是在某可分Banach空间B上取值的独立随机变量序列,S_n=X_1+…+X_n,n≥1,对某00和>1,定义P_n(ε)=P(‖S_n‖/n~(1/p)≥ε)。本文的目的是研究当n→+∞时,P(ε)→0的速度,在Banach空间上推广了Heyde和Rohatgi的结果;同时,本文还讨论了P_n(ε)→0的速度与S_n/n~(1/p)→0 a.s.的关系问题。     

The Compact Law of the Iterated Logarithm for Multivariate Stochastic Approximation Algorithms

Abstract

We consider a sequence (Z _n ) _n≥1 defined by a general multivariate stochastic approximation algorithm and assume that (Z _n ) converges to a solution z* almost surely. We establish the compact law of the iterated logarithm for Z _n by proving that, with probability one, the limit set of the sequence (Z _n  ? z*) suitably normalized is an ellipsoid. We also give the law of the iterated logarithm for the l ^p norms, p ∈ [1, ∞], of (Z _n  ? z*).     

A note on some laws of the iterated logarithm

Some function space laws of the iterated logarithm for Brownian motion with values in finite and infinite dimensional vector spaces are shown to follow from Hincin's classical law of the iterated logarithm and some martingale techniques. A law of the iterated logarithm for Brownian motion in a differentible manifold is also stated.     

Laws of Large Numbers for Exchangeable Random Sets in Kuratowski-Mosco Sense

Abstract

Most of the results for laws of large numbers based on Banach space valued random sets assume that the sets are independent and identically distributed (IID) and compact, in which Rådström embedding or the refined method for collection of compact and convex subsets of a Banach space plays an important role. In this paper, exchangeability among random sets as a dependency, instead of IID, is assumed in obtaining strong laws of large numbers, since some kind of dependency of random variables may be often required for many statistical analyses. Also, the Hausdorff convergence usually used is replaced by another topology, Kuratowski-Mosco convergence. Thus, we prove strong laws of large numbers for exchangeable random sets in Kuratowski-Mosco convergence, without assuming the sets are compact, which is weaker than Hausdorff sense.