半参数回归模型的误差方差的小波估计

考虑半参数回归模型yi=Xi'β＋g（ti）＋ei,1≤i≤n,其中β∈Rd为未知参数,g（t）为[0,1]上的未知Borel函数,xi为Rd上的随机设计,{ei}为i.i.d．随机误差本文构造了误差方差σi2=var（ei）的小波估计■,得到了■的渐近正态性,同时构造了var(ei2)的小波估计■,并且证明了■的弱相合性,由此可知■依分布收敛于N（0,1）,这一结果可用于构造σ2的大样本区间估计或对σ~2进行大样本检验。     

强相依数据的函数系数部分线性模型的估计

本文讨论在数据是强相依的情况下函数系数部分线性模型的估计．首先,采用局部线性方法,给出该模型函数项函数的估计;然后,使用两阶段方法给出系数函数的估计．并且讨论了函数项函数估计的渐近正态性,以及系数函数估计的弱相合性和渐近正态性.模拟研究显示,这些估计是较为理想的．     

带有异方差的部分线性回归模型的B样条估计

考虑一带有异方差的固定设计部分线性回归模型yij=X'ijβ+g(tij)+εij,i=1,2…,k:j=1,2,…,ni,和sum from i=1 to kni=n,其中yij为响应变量,β=(β1,…,βp)’是未知的参数向量,g(·)是未知的函数,Xij=(Xij1,…,Xijp)’和tij∈[0,1]为已知的非随机设计点,εij为均值0,方差是σi2的随机误差,其中σi2可能不同.通过B样条级数近似非参数分量,构造了参数分量β的一个半参数广义最小二乘估计.在一些矩条件下,导出了此半参数广义最小二乘估计的渐近分布,大多数在实际中遇到的误差分布都满足这些矩条件.另外,也构造了半参数广义最小二乘估计的渐近协方差矩阵的一个相合估计,还讨论了非参数分量的B样条估计.所有这些大样本性质都是在k趋于无穷大,ni有限时导出的.这些结果能被用来做渐近有效的统计推导.     

带有异方差的部分线性回归模型的B样条估计

考虑一带有异方差的固定设计部分线性回归模型yij=x'ijβ+g(tij)+εij,i=1,2,…,k,j=1,2,…,ni,和∑ni=n,其中yij为响应变量,β=(β1,…,βp)'是未知的参数向量,g(.)是未知的函数,xij=(xij1,…,xijp)'和tij∈[0,1]为已知的非随机设计点,εij为均值0,方差是σ2i的随机误差,其中σ2i可能不同.通过B样条级数近似非参数分量,构造了参数分量β的一个半参数广义最小二乘估计.在一些矩条件下,导出了此半参数广义最小二乘估计的渐近分布,大多数在实际中遇到的误差分布都满足这些矩条件.另外,也构造了半参数广义最小二乘估计的渐近协方差矩阵的一个相合估计,还讨论了非参数分量的B样条估计.所有这些大样本性质都是在k趋于无穷大,n.有限时导出的.这些结果能被用来做渐近有效的统计推导.     

半参数回归的线性小波光滑

考虑半参数回归模型上未知函数,ｙｉ＝ｘ_ｉβ＋ｇ（ｔｉ）＋ｅｉ,１≤ｉ≤ｎ,ｇ（·）为Ｒ~１上未知参数,β∈Ｒ~ｐ为待估参数, Ａｎｔｏｎｉａｄｓ［３］中给出了非参数回归模型的小波估计,借鉴［３］我们利用偏残差法给出了β、ｇ（·）的小波估计β、ｇ（·）．本文研究了β、ｇ（·）的弱相合性及它们偏差和方差的渐近性质,并且得到了β的渐近正态性．     

半参数回归模型的误差方差的小波估计

考虑半参数回归模型y i=x i' β +g(t i)+e i,1 i n,其中β∈R d为未知参数,g(t)为[0,1]上的未知Borel函数,x i为R d上的随机设计, {e i}为i.i.d.随机误差. 本文构造了误差方差σi 2=var (e i)的小波估计 n 2,得到了 n 2的渐近正态性, 同时构造了var(e i 2)的小波估计 n 2,并且证明了 n 2的弱相合性, 由此可知 依分布收敛于N(0,1), 这一结果可用于构造σ 2的大样本区间估计或对σ 2进行大样本检验.     

线性约束下部分线性模型中估计的渐近性质

考虑回归模型yi=x′iβ+ g(ti) + ei, 0 ≤i ≤nr=Rβ其中(xi,ti)是固定非随机设计点列,xi=(xi1,…,xip)′,β=(β1,…,βp)′(p 1) ,g是定义在[0 ,1]上的未知函数,β是未知待估参数,0≤ ti≤1i,ei 是i.i.d随机误差,且Eei=0 ,Ee2i=σ2 <∞.r是一个J维向量,R是一个J* p列满秩矩阵,基于g的估计取一个非参数权估计,本文讨论了在线性约束下β的最小二乘估计的相合性及渐近正态性.     

相协样本半参数回归模型估计的强相合性

考虑半参数回归模型Y^(j)(xin,lin)=tinβ g(xin) e^(j)(xin)，1≤j≤m，1≤i≤n,利用最小二乘法和权函数估计方法，定义β，g的估计量βm,n和gm,n(x)，在负相依样本及较弱的条件下证明了这些估计的强相合性，得到了与独立情形一致的结论．     

一个部份自回归模型的渐近有效性<英>

本文考虑部分自回归模型 X_t=X_(t-1)β g(U_t) ε_t,t≥1.这里g是一未知函数,β是一待估参数,ε_j是具有0均值和方差σ~2的i.i.d.误差,U_t i.i.d.服从[0,1]上均匀分布.本文首先给出了相合估计的收敛阶和Takeuchi意义下渐近有效界.同时给出了β最小二乘估计是有效的充要条件.最后证明了MLE是渐近有效的.     

半参数回归模型小波估计的矩相合性

用小波方法,考虑半参数回归模型y_i=X_i~Tβ+g(t_i)+ε_i(1≤i≤n),其中β∈R~d为未知参数,g(t)为[0,1]上未知的Borel可测函数,X_i为R~d上的随机设计,随机误差{ε_i}为鞅差序列,{t_i}为[0,1]上的常数序列.得到参数及非参数的小波估计量的q-阶矩相合性.     

Asymptotic properties of wavelet estimators in partially linear errors-in-variables models with long-memory errors

While the random errors are a function of Gaussian random variables that are stationary and long dependent, we investigate a partially linear errors-in-variables (EV) model by the wavelet method. Under general conditions, we obtain asymptotic representation of the parametric estimator, and asymptotic distributions and weak convergence rates of the parametric and nonparametric estimators. At last, the validity of the wavelet method is illuminated by a simulation example and a real example.     

The nonparametric estimation of long memory spatio-temporal random field models

This paper considers the local linear estimation of a multivariate regression function and its derivatives for a stationary long memory(long range dependent) nonparametric spatio-temporal regression model.Under some mild regularity assumptions, the pointwise strong convergence, the uniform weak consistency with convergence rates and the joint asymptotic distribution of the estimators are established. A simulation study is carried out to illustrate the performance of the proposed estimators.     

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.     

PA误差下回归函数小波估计的渐近性质

本文研究了回归函数小波估计的渐进性质的问题.利用概率不等式方法,获得了函数g(·)的小波估计量的r-阶矩相合,依概率收敛和强收敛以及渐进正态性的结果,所获的结果推广了其他混合相依下的相应结果.     

多元非参数计量经济模型的变窗宽局部线性估计

在随机设计(模型中所有变量为随机变量)下,提出了非参数计量经济模型的变窗宽局部线性估计,并利用概率论中大数定理和中心极限定理,在内点处证明了它的一致性和渐近正态性.它在内点处的收敛速度达到了非参数函数估计的最优收敛速度.     

Asymptotic normality in random graphs with given vertex degrees

We consider random graphs with a given degree sequence and show, under weak technical conditions, asymptotic normality of the number of components isomorphic to a given tree, first for the random multigraph given by the configuration model and then, by a conditioning argument, for the simple uniform random graph with the given degree sequence. Such conditioning is standard for convergence in probability, but much less straightforward for convergence in distribution as here. The proof uses the method of moments, and is based on a new estimate of mixed cumulants in a case of weakly dependent variables. The result on small components is applied to give a new proof of a recent result by Barbour and Röllin on asymptotic normality of the size of the giant component in the random multigraph; moreover, we extend this to the random simple graph.     

Asymptotic autonomous attractors for a stochastic lattice model with random viscosity

We study forward asymptotic autonomy of a pullback random attractor for a non-autonomous random lattice system and establish the criteria in terms of convergence, recurrence, forward-pullback absorption and asymptotic smallness of the discrete random dynamical system. By applying the abstract result to both non-autonomous and autonomous stochastic lattice equations with random viscosity, we show the existence of both pullback and global random attractors such that the time-component of the pullback attractor semi-converges to the global attractor as the time-parameter tends to infinity.     

关于若干重要数列的收敛速度及其渐近性

对极限值为重要常数e、π及欧拉常数γ的数列的收敛速度及渐近性进行讨论,我们很惊奇地发现它们当中的大部分数列具有完全相同的收敛速度及其渐近性.     

Asymptotics of R-, MD- and LAD-estimators in linear regression models with long range dependent errors

Summary This paper establishes the uniform closeness of a weighted residual empirical process to its natural estimate in the linear regression setting when the errors are Gaussian, or a function of Gaussian random variables, that are strictly stationary and long range dependent. This result is used to yield the asymptotic uniform linearity of a class of rank statistics in linear regression models with long range dependent errors. The latter result, in turn, yields the asymptotic distribution of the Jaeckel (1972) rank estimators. The paper also studies the least absolute deviation and a class of certain minimum distance estimators of regression parameters and the kernel type density estimators of the marginal error density when the errors are long range dependent.Research of this author was partly supported by the NSF grant: DMS-9102041     

Nonparametric regression on random fields with random design using wavelet method

We consider non-linear wavelet-based estimators of spatial regression functions with (known) random design on strictly stationary random fields, which are indexed by the integer lattice points in the \(N\)-dimensional Euclidean space and are assumed to satisfy some mixing conditions. We investigate their asymptotic rates of convergence based on thresholding of empirical wavelet coefficients and show that these estimators achieve nearly optimal convergence rates within a logarithmic term over a large range of Besov function classes \(B^{s}_{p,q}\). Therefore, wavelet estimators still achieve nearly optimal convergence rates for random fields and provide explicitly the extraordinary local adaptability.