LARGE DEVIATIONS FOR STATIONARY Φ-MIXING SEQUENCES IN τ-TOPOLOGY

The results of Brye on large deviations for empirical measures of stationary φ-mixing sequences are extended. Brye states his results in the usual weak topology on the space ofprobability measures. In this paper, under somewhat weaker assumptions than those of Bryc,the author extends Brye's results by taking the finer topology which is generated by the integrals over bounded measurable functions.

Complete Convergence Properties of the Sums for -mixing Sequences of Random Variables

This paper discusses complete convergence properties of the sums of -mixing random sequences.As a result,we improve the corresponding results of Wu Qunying(2001). And extended the Baum and Katz complete convergence to the case of -mixing random sequences by moment inequality and truncating without necessarily adding any extra conditions.

Strong Law of Large Numbers and Complete Convergence for Sequences of -Mixing Random Variables

We give some theorems of strong law of large numbers and complete convergence for sequences of φ-mixing random variables. In particular, Wittmann＇s strong law of large numbers and Teicher＇s strong law of large nnumbers for independent random variables are generalized to the case of φ -minxing random variables.

Complete Convergence and Weak Law of Large Numbers for <i><span style="text-decoration:overline;">ρ</span></i>-Mixing Sequences of Random Variables

In this paper, the complete convergence and weak law of large numbers are established for ρ-mixing sequences of random variables. Our results extend and improve the Baum and Katz complete convergence theorem and the classical weak law of large numbers, etc. from independent sequences of random variables to ρ-mixing sequences of random variables without necessarily adding any extra conditions.

Complete Convergence for Weighted Sums of φ-mixing Sequence

In this paper, the complete convergence and strong law of large numbers for weighted sums of φ-mixing sequence with different distribution are investigated under some weaker moment conditions. Our results extend ones of independent sequence with identical distribution to the case of φ-mixing sequence with different distribution.

A Note on the Almost Sure Central Limit Theorem for Partial Sums of ρ^(−)-Mixing Sequences

Let be a strictly stationary sequence of ρ?-mixing random variables. We proved the almost sure central limit theorem, containing the general weight sequences, for the partial sums , where , . The result generalizes and improves the previous results.

Joint asymptotic distribution of exceedances point process and partial sum of stationary Gaussian sequence

Let {Xi}i=1^∞ be a standardized stationary Gaussian sequence with covariance function τ（n） =EX1Xn＋1, Sn =∑i=1^nXi,and X^-n=Sn/n.And let Nn be the point process formed by the exceedances of random level （x/√2 log n＋√2 log n-log（4π log n）/2√log n） √1-τ（n） ＋ X^-n by X1,X2,…, Xn. Under some mild conditions, Nn and Sn are asymptotically independent, and Nn converges weakly to a Poisson process on （0,1].

The asymptotic relation between the first crossing point and the last exit time of Gaussian order statistics sequences

In this paper,we study the asymptotic relation between the first crossing point and the last exit time for Gaussian order statistics which are generated by stationary weakly and strongly dependent Gaussian sequences.It is shown that the first crossing point and the last exit time are asymptotically independent and dependent for weakly and strongly dependent cases,respectively.The asymptotic relations between the first crossing point and the last exit time for stationary weakly and strongly dependent Gaussian sequences are also obtained.

Strong Convergence Rates of Double Kernel Estimates of Conditional Desity Under Stationary Sequences

In the paper,we study the strong convergence rates of double kernel estimates of conditional density under stationary sequences.

The maxima and sums of multivariate non-stationary Gaussian sequences

Let {Xkl,…, Xkp, k≥ 1} be a p-dimensional standard （zero-means, unit-variances）non-stationary Gaussian vector sequence. In this work, the joint limit distribution of the maximaof {Xkl,…, Xkp, k 〉 1}, the incomplete maxima of those sequences subject to random failureand the partial sums of those sequences are obtained.

A Note on the Almost Sure Central Limit Theorem in the Joint Version for the Maxima and Partial Sums of Certain Stationary Gaussian Sequences

Considering a sequence of standardized stationary Gaussian random variables, a universal result in the almost sure central limit theorem for maxima and partial sum is established. Our result generalizes and improves that on the almost sure central limit theory previously obtained by Marcin Dudzinski [1]. Our result reaches the optimal form.

Almost sure limit theorem for the maximum of a class of quasi-stationary sequences

This paper investigates the problem of almost sure limit theorem for the maximum of quasi-stationary sequence based on the result of Turkman and Walker. We prove an almost sure limit theorem for the maximum of a class of quasi-stationary sequence under weak dependence conditions of D （uk, un） and αtm,ln = 0 （（log log n）-（1＋ε））.

LARGE DEVIATIONS FOR SOME DEPENDENT SEQUENCES

Let （Xi） be a martingale difference sequence and Sn=∑^ni=1Xi Suppose （Xi） i=1 is bounded in L^p. In the case p ≥2, Lesigne and Volny （Stochastic Process. Appl. 96 （2001） 143） obtained the estimation μ（Sn 〉 n） ≤ cn^-p/2, Yulin Li （Statist. Probab. Lett. 62 （2003） 317） generalized the result to the case when p ∈ （1,2] and obtained μ（Sn 〉 n） ≤ cn^l-p, these are optimal in a certain sense. In this article, the authors study the large deviation of Sn for some dependent sequences and obtain the same order optimal upper bounds for μ（Sn 〉 n） as those for martingale difference sequence.

Convergence Rate of Estimator forNonparametric Regression Model under ρ-mixing Errors

In this paper, we investigate the nonparametric regression model based on ρ-mixing errors, which are stochastically dominated by a nonnegative random variable. Weobtain the convergence rate for the weighted estimator of unknown function g（x） in pth-mean, which yields the convergence rate in probability. Moreover, an example of the nearestneighbor estimator is also illustrated and the convergence rates of estimator are presented.

Rates of uniform convergence for empirical processes of strictly stationary β-mixing sequences indexed by an unbounded class of functions

Assume that {Xn} is a strictly stationary β-mixing random sequence with the β-mixing coefficient βk = O(k-r), 0 < r ≤1. Yu (1994) obtained convergence rates of empirical processes of strictly stationary β-mixing random sequence indexed by bounded classes of functions. Here, a new truncation method is proposed and used to study the convergence for empirical processes of strictly stationary β-mixing sequences indexed by an unbounded class of functions. The research results show that if the envelope of the index class of functions is in Lp, p > 2 or p > 4, uniform convergence rates of empirical processes of strictly stationary β-mixing random sequence over the index classes can reach O((nr/(l+r)/logn)-1/2) or O((nr/(1+r)/ log n)-3/4) and that the Central Limit Theorem does not always hold for the empirical processes.

Ng-Perron单位根检验在径流序列非平稳性分析中的应用

在全球气候变化和高强度人类活动的共同影响下,许多流域天然水循环过程受到破坏。径流序列呈现明显的非平稳特性,给水资源规划、管理、预测和调控带来一定的挑战。揭示径流序列的非平稳特性可以有效应对全球气候变化下的复杂水问题,对降低水文分析难度和提高径流预测精度具有十分重要的意义。研究以汾河上游兰村站为研究对象,分析该站1958-2016年年径流和月径流序列是否平稳。首先从随机水文学角度,采用Mann-Kendall检验法和小波分析法识别径流序列的趋势、突变和周期特征。在此基础上,从统计水文学角度引入Ng-Perron单位根检验方法。通过Mann-Kendall趋势检验和散点图法选择合适的检验方程,对径流序列进行广义最小二乘法(Generalized Least Squares,GLS)退势,并利用修正的信息准则(Modified information criterion,MIC)计算最优时间滞后阶数,判别径流序列是否具有非平稳性。结果显示,径流序列存在趋势、突变和周期成分,为非平稳径流序列。同时Ng-Perron单位根检验表明,该站年、月径流序列在1%显著性水平上具有非平稳特性。相较传统单位根检验方法,Ng-Perron单位根检验采用更为稳健的修正检验统计量,显著调整小样本情况下水平扭曲的现象,具有更好检验水平和功效,因而可以得到更合理的检验结果。研究成果为径流序列非平稳性检验理论的进一步改进及径流预测模型发展与应用提供参考。

A LOW OF THE ITERATED LOGARITHM FOR DOUBLE ARRAY SUM OF φp-MIXING SEQUENCE

I. INTRODUCTION AND SOME LEMMASLet{εi} be independent random variables, E（εi） = 0, E（εi2） =σ2, sup E[εi|r【∞ for some r】2, {ani} be a double array of constants satisfying proper conditions. Ref. [1] gave

Complete convergence of ρ-mixing sequence

Let {Xn, n≥1} be a sequence of random variables and let Sn=∑1≤i≤nXi,n-=σ（Xi1≤i≤n）,n+=σ（Xi,i≥n）,n≥1.

考虑海平面上升影响的极值水位计算

本研究基于非平稳序列极值理论,定量分析极端水位事件年超越概率受海平面上升的影响;以工程设计使用年限内极端水位发生概率作为控制条件,构建考虑海平面上升的极值水位计算方法;结合平均海平面的长期变化过程,推算海平面上升下的极值水位。基于全球10个验潮站历史水位观测资料,验证历史平均海平面长期变化与高、低水位耿贝尔分布位置参数变化的一致性以及构建方法的合理性。结合政府间气候变化专门委员会对海平面上升的预测,推算和对比分析不同海平面上升情景下的极值水位,并评估相应极值水位在当前极值分布中的重现期。

随机波动模型最大值的极限定理

考虑一类经典的随机波动模型,其波动性由平稳的对数高斯随机序列进行刻画.假设该高斯随机序列相关系数函数rn满足lim_(n→∞)rnln_(n)=r∈[0,∞],利用点过程的相关方法,证明了几个关于该随机波动模型最大值及次序统计量的极限定理,推广了文献中存在的一些经典结论.