方差无穷非线性自回归序列的自加权L_1估计

对具有无穷方差的非线性自回归序列x_t=φ(x_(t-1),x_(t-2),…,x_(t-p),θ) ε_t,E(ε_t~2)=∞,利用局部二次近似和连续函数空间C(R~q)上弱收敛随机过程最小点的渐近性质,证明了若存在δ≥1,使得E|ε_t|~δ<∞成立,则θ满足一定条件的自加权L_1估计θ_(L_1)是渐近正态估计,Wald检验统计量也具有通常的x~2分布,为模型的统计推断提供了理论基础.     

重尾扰动下近非平稳自回归模型的Bootstrap推断

考虑近非平稳一阶自回归模型,Xt=θXt-1+ut,其中θn=1-γ/n,γ为一固定常数,{ut}为一重尾随机扰动项.对θn及其最小二乘估计(θ)n,采用bootstrap再抽样方法逼近(θ)n-θn的分布,并证明了该再抽样方法的渐近有效性.     

具有外生变量部分线性自回归模型的样条估计

考虑自回归模型Yt=θTXt+g(Zt)+Et,t=1,…,n,其中Xt=(Y-1,…,Yt-d)T,Zt为实值外生随机变量,θ=(θ1,…,θd)T为待估参数向量,g为未知非参数光滑函数.基于多项式样条方法,在一定的条件下,给出了θ的估计的渐近正态性,得到了g的估计的收敛速度.模拟例子验证了所得的理论结果.     

非线性自回归模型的分位数推断

考虑非线性自回归模型x_t=f (x_(t-1),···, x_(t-p),θ)+ε_t,其中f (·)为R~p上的实值可测函数,θ为q维未知参数,{ε_t}为随机误差.构造了θ的分位数估计,并证明了该估计的渐近正态性,最后通过数值模拟,说明了该估计的稳健和有效性.     

加权平方损失下伽玛分布族Γ(θ,1/2)参数θ的EB估计

在加权平方损失函数下讨论了伽玛分布族T(θ,1/2)参数θ的经验Bayes(EB)估计,并讨论了EB估计的收敛速度问题,在一定条件下,收敛速度可充分接近于1.     

对称Stable过程的多重自相交局部时研究

设X_t是取值于R²中阶为β的对称stable过程.本文主要研究X_t的k-重自相交局部时αk (x;t)及其重整化自相交局部时αk (0;t),其中k≥2.首先,当x≠0时,对所有p> 0,考虑αk(x;t)在L^p(Ω)中的存在性.其次,分别给出α_k(x;t)关于时间变量t和空间变量x的H?lder连续性条件.最后,由于α_k(0;t)不存在,故考虑其重整化局部时?_k(0;t)在L²(Ω)中的存在性问题.     

关于Neyman-Pearson基本引理的几个注记

本文探讨了Neyman-Pearson基本引理.通过论证总体参数θ只有θ₀或θ₁两种可能时最优检验功效函数的唯一性,得到了两种假设T₁:θ=θ₀←→θ=θ₁和T₂:θ=θ₁←→θ=θ₀各自对应最优检验的两类错误概率可以互换的结论.     

空间非齐次白噪声驱动的分数阶随机热方程的矩估计

该文主要研究如下伴有Cauchy初值条件的分数阶随机热方程■其中a∈(1,2]为算子D_δ^α的阶数,δ(|δ|≤2-α)称为偏度参数,扩散系数g(·):R→R是非随机的可测函数:?²/?_t?_xw_ρ(t,x)表示空间非齐次白噪声,在关于非齐次布朗单w_ρ(t,x)催化测度ρ的适当假设下,证明了该方程解的存在性、唯一性和H?lder连续性.同时,也证明了方程解的矩估计.     

误差为FCA过程的线性模型HD估计的渐近正态性

考虑线性回归模型y=x^Tβ+e₁其中误差e是函数系数自回归（FCA）过程.本文研究该模型未知参数的Huber-Dutter估计的渐近性质,在合理的条件下,证明了这些估计量以n^-（1/2）速度渐近于正态分布.     

带有重尾扰动项的非线性自回归模型

讨论在非线性自回归模型中平稳解边际尾概率与扰动项尾概率之间的关系. 证明了在保证模型平稳遍历的条件下, 一维平稳解的边际分布具有重尾概率性质, 并且与扰动项重尾概率指标相同.     

Efficient estimation and variable selection for infinite variance autoregressive models

In this paper, a self-weighted composite quantile regression estimation procedure is developed to estimate unknown parameter in an infinite variance autoregressive (IVAR) model. The proposed estimator is asymptotically normal and more efficient than a single quantile regression estimator. At the same time, the adaptive least absolute shrinkage and selection operator (LASSO) for variable selection are also suggested. We show that the adaptive LASSO based on the self-weighted composite quantile regression enjoys the oracle properties. Simulation studies and a real data example are conducted to examine the performance of the proposed approaches.     

THE CONSTRUCTION OF DENUMERABLE q-PROCESSES IN RANDOM ENVIRONMENTS SATISFYING (F) OR (B)

This article is a continuation of [9]. Based on the discussion of random Kol-mogorov forward (backward) equations, for any given q-matrix in random environment,Q(θ) = (q(θ; x, y), x, y ∈ X), an infinite class of q-processes in random environments sat-isfying the random Kolmogorov forward (backward) equation is constructed. Moreover,under some conditions, all the q-processes in random environments satisfying the random Kolmogorov forward (backward) equation are constructed.     

Inference for Spatial Autoregressive Models with Infinite Variance Noises

A self-weighted quantile procedure is proposed to study the inference for a spatial unilateral autoregressive model with independent and identically distributed innovations belonging to the domain of attraction of a stable law with index of stability α, α ∈ (0, 2]. It is shown that when the model is stationary, the self-weighted quantile estimate of the parameter has a closed form and converges to a normal limiting distribution, which avoids the difficulty of Roknossadati and Zarepour (2010) in deriving their limiting distribution for an M-estimate. On the contrary, we show that when the model is not stationary, the proposed estimates have the same limiting distributions as those of Roknossadati and Zarepour. Furthermore, a Wald test statistic is proposed to consider the test for a linear restriction on the parameter, and it is shown that under a local alternative, the Wald statistic has a non-central chisquared distribution. Simulations and a real data example are also reported to assess the performance of the proposed method.     

Upper functions for positive random functionals. I. General setting and Gaussian random functions

In this paper we are interested in finding upper functions for a collection of real-valued random variables {Ψ(χ _θ ), θ ∈ Θ}. Here {χ _θ , θ ∈ Θ} is a family of continuous random mappings, Ψ is a given sub-additive positive functional and Θ is a totally bounded subset of a metric space. We seek a nonrandom function U: Θ → ?₊ such that sup _θ∈Θ{Ψ(χ _θ ) ? U(θ)}₊ is “small” with prescribed probability. We apply the results obtained in the general setting to the variety of problems related to Gaussian random functions and empirical processes.     

混合缺失机制下两总体差异的半经验似然置信区间

假定两个总体x与y均有数据缺失,它们的分布函数分别为F(·)与G_θ(·),其中F(·)未知,G_θ(·)的概率密度函数g_θ(·)形式已知,仅依赖于一些未知的参数,利用Fractional填补法填补缺失值,在一定的条件下证明了缺失数据下两总体差异指标的半经验似然比统计量的渐近分布为x_1~2,由此可构造两总体差异指标的经验似然置信区间.     

带分布时滞中立型微分方程正解的存在性

主要运用Banach压缩映像原理,研究了分布时滞中立型微分方程ddty(t)+∫abP(t,θ)y(t-θ)dθ+∫cdQ(t,θ)y(t-θ)dθ=0正解的存在性问题,获得了正解存在的充分条件,推广了相关文献的结果.     

NA误差下半参数回归模型估计的相合性

对半参数回归模型:Y(xin,tin)=tinb+g(xin)+e(xin),1≤j≤m,1≤i≤n,本文在NA相依样本下讨论了g的加权估计及b的最小二乘估计的强相合性与r(>2)阶平均相合性,使得文献犤2犦在独立样本下的相应结果得到推广     

幂平均不等式的最优值

设Mn[r](a)为a的r阶幂平均,0<α<θ<β,那么满足不等式[Mn[α](a)]1-λ.[Mn[β](a)]λ≤Mn[θ](a)的最大实数λ是λ≥{1+(β-θ)/[m(θ-α)]}-1.这里m=min{[2+(n-2)tβ]/[2+(n-2)tα],t∈R++};满足反向不等式的最小实数λ是λ=[β(θ-α)]/[θ(β-α)].本文的方法基于优势理论与解析技巧,对于建立不等式的最优化思想作了尽可能多的展示.作为应用,得到了一些涉及和、积分与矩阵的新不等式(含Hardy不等式的推广与加强).     

二维Qausi-Geostrophic方程的几何约束与非爆炸性

研究二维Qausi-Geostrophic(QG)方程水平集的动态演化过程.在"涡线"上"涡度"的最大值与全局"涡度"最大值可比的假设下,得到若"涡线"的长度L(t)被O(ln ln ||▽~⊥θ(·,t)||_∞)控制(被O(ln ln ||▽~⊥θ(·,t)||_∞)控制),曲率最大值K(t)与L(t)的乘积被O(ln ln ln ||▽~⊥θ(·,t)||_∞)控制(被O(ln ln ||▽~⊥θ(·,t)||_∞)控制),则||▽~⊥θ(·,t)||_∞)的增长阶数最多可达四重指数阶,这样二维QG方程在有限时间内无爆炸发生.