NA序列部分和的完全收敛性

本文讨论了非平稳NA随机变量序列部分和的完全收敛性,获得了一般形式的完全收敛速度与矩条件之间的等价关系,其结果与独立情形一致,从而证实了NA序列与独立序列有着极为类似的完全收敛性．     

同分布NA序列的强收敛性

本文讨论了NA序列极限理论中的一些基本问题．首先证明了对称化的NA族仍为NA族，建立了基于通常截尾术的NA序列的三级数定理；并在此基础上给出了同分布NA序列的与iid序列完全一致的Marcinkiewicz强大数律，还得到了关于同分布NA序列的与iid序列极为相似的有关完全收敛性的一系列等价性命题．     

平稳NA随机变量序列中偏差的下界估计

本文在比较一般的条件下得到了平稳NA序列的中偏差下界估计，进而得到平稳NA序列的中偏差原理。     

NA样本下回归函数估计的收敛速度

在误差为NA序列的条件下,研究了固定设计点列情形下非参数回归函数一般权函数的非参数估计,并在一些基本条件下给出了估计的一致最优强收敛速度.     

非平稳ND序列部分和的精确渐近性

利用中心极限定理和概率不等式,本文建立了非平稳ND序列部分和的精确渐近性,得到了与非平稳NA序列情形下相同的结果.     

NA随机变量序列的Egorov型强大数律的等价条件

本文研究了NA随机变量的Egorov型强大数律.利用NA随机变量的概率不等式,得到了NA随机变量序列的Egorov型强大数律的一些等价条件,所获结果推广和改进了在独立随机变量序列的Egorov的结果和在NA随机变量序列已有的一些结果.     

NA序列的矩不等式与弱收敛

对NA随机变量序列建立了关于部分和S_n的一个概率不等式,关于|S_n|与max(1≤k≤n)|S_k|的P≥2阶矩的有关不等式,并在一般的条件下证明了强平稳NA序列的一个弱不变原理。     

NA列递归密度核估计的相合性

本文在一定的条件下证明了基于NA样本序列的递归型密度核估计的均方相合性和逐点强相合性，作为在可靠性问题中的应用，利用NA样本构造了生存函数和失效率函数的估计，并讨论了相应的逐点强相合性。     

同分布NA序列的一个弱大数律

本文从讨论同分布NA序列弱大数律的成立条件入手，不仅给出了它的一个弱大数律，而且得到了这类条件的三种等价形式，由此揭示出同分布NA序列在弱大数律方面与独立同分布（i．i．d．）序列的不同之处，因之与这两者在强大数律方面有完全相同的性状形成了对比。     

非平稳NA序列中心极限定理的一些结果

本文讨论了非平稳同分布ＮＡ序列的渐近正态问题，给出了两个中心极限定理，以往的文献在讨论ＮＡ序列的这些问题时，多加有强平稳的限制；但是大量问题所出现的ＮＡ序列却多为非平稳的，因此有开展研究的必要。本文在寻求摆脱平稳性限制的途径方面作了有益的尝试，所得的定理不仅可解决一大类非平稳ＮＡ序列的渐近正态问题，而且将以往的强平稳场合的结果包含为特例，具有一定的理论意义与应用价值。     

Extremal clustering in non-stationary random sequences

Extremes - It is well known that the distribution of extreme values of strictly stationary sequences differ from those of independent and identically distributed sequences in that extremal...     

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In this paper, by using central limit theorem of ND sequences and probability inequality, the precise asymptotics for partial sums of nonstationary ND sequences is investigated, and the same results with it under that of NA sequences are obtained.     

Forecast horizon in nonstationary Markov decision problems

The paper extends the concept of decision and forecast horizons from classes of stationary to classes of nonstationary Markov decision problems. The horizons are explicitly obtained for a family of inventory models. The family is indexed by nonstationary Markov chains and deterministic sequences. For the proof only reference to simlier work on the stationary case is made.     

NA序列的重对数矩收敛的精确渐近性

假设{X_n,n≥1}为一列严平稳的NA随机变量,期望为零,方差有限.设S_n=∑_(i=1)~n∑X_i,M_n=max_(1≤i≤n)|S_i|.在适当的条件下,得到了一类NA序列部分和部分和最大值重对数矩收敛的精确渐近性.     

NA列加权乘积和的完全收敛性

本文讨论了NA和几类加权部分和及加权乘积和的完全收敛性，其中部分结果要优于iid列的已知结论。     

Central limit theorems under weak dependence

This article is motivated by a central limit theorem of Ibragimov for strictly stationary random sequences satisfying a mixing condition based on maximal correlations. Here we show that the mixing condition can be weakened slightly, and construct a class of stationary random sequences covered by the new version of the theorem but not Ibragimov's original version. Ibragimov's theorem is also extended to triangular arrays of random variables, and this is applied to some kernel-type estimates of probability density.     

Nonlinear approximation with nonstationary Gabor frames

We consider sparseness properties of adaptive time-frequency representations obtained using nonstationary Gabor frames (NSGFs). NSGFs generalize classical Gabor frames by allowing for adaptivity in either time or frequency. It is known that the concept of painless nonorthogonal expansions generalizes to the nonstationary case, providing perfect reconstruction and an FFT based implementation for compactly supported window functions sampled at a certain density. It is also known that for some signal classes, NSGFs with flexible time resolution tend to provide sparser expansions than can be obtained with classical Gabor frames. In this article we show, for the continuous case, that sparseness of a nonstationary Gabor expansion is equivalent to smoothness in an associated decomposition space. In this way we characterize signals with sparse expansions relative to NSGFs with flexible time resolution. Based on this characterization we prove an upper bound on the approximation error occurring when thresholding the coefficients of the corresponding frame expansions. We complement the theoretical results with numerical experiments, estimating the rate of approximation obtained from thresholding the coefficients of both stationary and nonstationary Gabor expansions.     

About Nonstationary Multiresolution Analysis and Wavelets

The characterization of orthonormal bases of wavelets by means of convergent series involving only the mother wavelet is known, as well as the characterization of wavelets which can be constructed from a stationary multiresolution analysis or a scaling function (see for example [11] and references therein). Here we show that under some asymptotic condition, these results remain true in the nonstationary case.     

Relative stability in strictly stationary random sequences

Relative stability results for weakly dependent and strongly mixing strictly stationary sequences are established. As a consequence, some infinite memory models, including ARCH(1) processes, are relatively stable.