指数分布位置参数和分位点的估计

§1 引言 设随机变量X服从指数分布E(u,σ),具有密度函数 此处-∞相似文献   

相依样本下非参数回归函数估计的强收敛速度

设(X,Y),(X_1,Y_1,),…,(X_n,Y_n)是一个平稳、φ—混合过程((X,Y)∈R~d×R,E|Y|~(s δ)<∞,s≥2,δ>0),用m(x)记E{Y|X=x},本文讨论了m(x)的如下估计m_n(x)的强收敛速度:     

一类统计量的强大数律和重对数律的精确极限性质

设{xn,m≥1}是独立同分布随机变量序列,EX1=0,EX12=1．设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．     

Precise Rates in the Law of Iterated Logarithm for the Moment of I.I.D. Random Variables

Let{X,Xn;n≥1} be a sequence of i,i.d, random variables, E X = 0, E X^2 = σ^2 〈 ∞.Set Sn=X1＋X2＋…＋Xn,Mn=max k≤n│Sk│,n≥1.Let an=O（1/loglogn）.In this paper,we prove that,for b〉-1,lim ε→0 →^2（b＋1）∑n=1^∞ （loglogn）^b/nlogn n^1/2 E{Mn-σ（ε＋an）√2nloglogn}＋σ2^-b/（b＋1）（2b＋3）E│N│^2b＋3∑k=0^∞ （-1）k/（2k＋1）^2b＋3 holds if and only if EX=0 and EX^2=σ^2〈∞.     

Precise Asymptotics in the Baum-Katz and Davis Laws of Large Numbers of ρ-mixing Sequences

Let {X,Xn;n ≥ 1} be a strictly stationary sequence of ρ-mixing random variables with mean zeros and finite variances. Set Sn =∑k=1^n Xk, Mn=maxk≤n｜Sk｜,n≥1.Suppose limn→∞ESn^2/n=：σ^2〉0 and ∑n^∞=1 ρ^2/d（2^n）〈∞,where d=2 if 1≤r〈2 and d〉r if r≥2.We prove that if E｜X｜^r 〈∞,for 1≤p〈2 and r〉p,then limε→0ε^2（r-p）/2-p ∑∞n=1 n^r/p-2 P{Mn≥εn^1/p}=2p/r-p ∑∞k=1（-1）^k/（2k＋1）^2（r-p）/（2-p）E｜Z｜^2（r-p）/2-p,where Z has a normal distribution with mean 0 and variance σ^2.     

不等方差线性模型中的AP统计量与信息比统计量

对线性模型：Y=Xβ ρ，E（ρ）=0,cov(ρ）=σ^2G,G=diag(g1,…gn)，探讨了单个数据点对σ^2的估计-↑σ^2，β的估计-↑β的广义方差|cov-↑β|及Fisher信息阵：σ^2X′G^-1X的影响，给出了度量准则和它的化简式及统计解释。     

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.     

随机狄里克莱级数在半平面上的增长性和值分布

研究了随机狄里克莱级数 f (s,ω) =∑∞n=1an Xne-λns在独立 (可不同分布 )随机变量序列{ Xn}满足(i) limn→∞E|Xn|>0 ,supn 1 E|Xn|p <∞ (p >1) ;(ii) limn→∞nλn=D <∞ ;(iii) limn→∞ln|an|λn=0等条件时的增长性和值分布 ,得到了比较好的结果     

On the Pointwisise L_p Convergence Rates of Nearest Neighbor Estimate of Nonparametric Regression Function

Let (X,y) be a R~d×R~1-valued radom vector with E(?)=E(Y|X=x) be the regression funvion of Y with respect to X.Su (X_i, Y_i),i=1, …,n, are iid samples drawn ftc, from (X,Y).(?)desired to estimate m(x) based on these samples,everoye discussed in (?) the pointwise convergence of the nearest neigthoor estimate m_n(X) (see (5) of the present paper). Ih this article we further study the rate of this eonyer-gence.It is shown that if there exists p≥2 sach that E 、|Y|<∞,then for suitabte choice of the weighte C_m (see(4) of the present paper).     

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.     

Projective normality of G.I.T. quotient varieties modulo finite groups

We prove that for any finite dimensional representation V of a finite group G of order n the quotient variety G??(V) is projectively normal with respect to descent of 𝒪(1)^?l where l = lcm{1,2,3,4,…,n}. We also prove that for the tautological representation V of the alternating group A_n the projective variety A_n??(V) is projectively normal with respect to the descent of the above line bundle.     

Evaluations of Parameters for the Optimization of S.S.O.R. and A.D.I. Preconditioning

The main goal of this paper is to give two ways to estimate the needed parameters in order to obtain the condition number of S.S.O.R. preconditioned matrices, namely, the algebraic matricial formulation of convexity Riesz theorem and the tridiagonal Fourier analysis. The improvement with respect to Axelsson's approach is explicitly given. Estimations of the condition number in the case of A.D.I. preconditioning is also considered.     

S.I.S.向量随机测度在弱拓扑及相容拓扑下的收敛性

本文主要研究ｓ．ｉ．ｓ．向量随机测度在弱拓扑及相容拓扑下的收敛性,给出了ｓ．ｉ．ｓ．向量随机测度在弱拓扑 容拓扑下的Ｖｉｔａｌｉ－Ｈａｌｍ－Ｓａｋｓ定理,作为应用,我们建立了Ｒ＾１－值有界可测函数关于Ｂａｎａｃｈ空间值ｓ．ｉ．ｓ．向量随机测度的随机积分的收敛定理,并得到了具ｔｙｐｅｐ的Ｂａｎａｃｈ空间中ｓ．ｉ．ｓ．向量随机测度的大数定律及中心极限定理。     

Optimization of the Relaxation Parameter for S.S.O.R. and A.D.I. Preconditioning

We present and compare several approaches for the optimization of the relaxation parameter both for A.D.I. and S.S.O.R. basic iteration and preconditioning conjugate gradient method. For each kind of preconditioning a detailed link between estimates of the spectral radius of the iteration matrix and of the condition number resulting from preconditioning is proposed. It allows to choose the best approach in order to obtain the optimal relaxation parameter and the corresponding optimal estimates either of the spectral radius of the iteration matrix and of the resulting condition mumber of the S.S.O.R. and A.D.I. preconditioning.     

三I表达式取最小值时的最优解

在本文中，针对蕴涵算子$R_0$,我们给出了当$A\rightarrow B$与$A^*(B^*)$给定时,求使三I表达式$(A\rightarrow B)\rightarrow(A^*\rightarrow B^*)$取最小值的全体$B^*(A^*)$之集的上确界(下确界)算法.并将上确界算法推广到多维多重的模糊推理中.从三I表达式取最值时求最优的$B^*(A^*)$的角度来看,本文是三I算法思想的延伸和完善.     

Class I graphs of nonnegative characteristic without special cycles

In this paper, we prove that every graph with maximum degree six that can be embedded in a surface of nonnegative characteristic is of Class I if it does not contain a 5- or 6-cycle with a chord, which extends some known results.     

定时截尾下指数分布的修正最大似然估计

本文在定时截尾情形下对指数分布提出了修正的最大似然估计，且把修正方法应用到定时截尾恒加试验和步加试验，模拟结果表明修正后的估计量的均方误差有了明显减少。     

Uniformly distributed sums of bernoulli variables

Sums of independent, identically distributed (iid) binomial variates have binomial distributions; yet it is possible to construct a sequence of binomial distributions over {0, 1} for variatesX ₁,X ₂, ... such that all partial sumsY _i =X ₁ + ... +X _i have uniform distributions. The price to pay is to give up the iid condition. Requiring the property of only one sum does not alleviate the situation much.It is also possible to generate on a computerm × n-matrices, of 0–1 binomial variates with uniformly distributed row and column sums of all major submatrices, but only for smallm andn. Even a three-dimensional 2 × 2 × 2 array can have a similar property.Other target distributions than the rectangular are possible, but cumbersome. An example with smaller variance is given.The results were needed for simulating the performance of some Operations Research algorithms.Dedicated to Peter Naur on the occasion of his 60th birthday     

Sufficient and necessary conditions for limit laws on lag sums of I. I. D. R. V. S

Let {X, X _n ;n>-1} be a sequence of i.i.d.r.v.s withEX=0 andEX ²=σ²(0 < σ < ∞). we obtain some sufficient and necessary conditions for