【作者】 赵月旭；

【导师】 林正炎；

【作者基本信息】 浙江大学 ， 概率论与数理统计， 2013， 博士

【摘要】 本文利用鞅方法和经验过程方法研究了相依样本的非参数统计与极限定理,建立了若干相关的结果.其一,利用鞅方法和分组技术,我们得到了ρ-混合样本核密度估计fn,K(x)的中心极限定理以及分布函数的估计Fn,K(x)=f-∞xfn,K(t)dt的中心极限定理；建立了混合相依样本条件期望的矩不等式.作为应用,研究了‖fn,K(x)-Efn,K(x)‖p在一致范数和积分Lp范数下的收敛速度；最后给出了|fn,K(x)-Efn,K(x)|在紧集以及整个实空间R上的强一致收敛速度.在适当的条件下,我们证明了关于独立同分布样本的最优结果对混合样本同样是最优的.其二,利用分块技巧,我们构造出一列m相依随机变量,由此将问题从相依转化为独立情形,然后借助于独立序列一些精细的概率不等式,给出了一类平稳过程核密度估计的渐近性质：首先建立了密度估计与其均值(真实密度)偏差的点态和一致最优弱收敛速度；其次得到了点态和一致最优强收敛速度.其三,我们建立了小波回归估计的中心极限定理.其次利用经验过程方法,强期望上界的熵估计,Bernstein不等式以及Talagrand不等式,我们得到了一致范数下几乎必然收敛的最优速度.其四,利用鞅方法和分块技巧,在较弱的相依速度条件下,我们建立了ρ-混合随机变量部分和在方差有限和无界情形下的强不变原理,改进了已有的一些结果.作为应用,得到了方差有限和无界情形下的一些重对数律型结果,并且解决了Shao(1993a)文中的一个猜想.最后,借助于概率不等式和弱不变原理,我们研究了负相依随机变量部分和与最大部分和完全矩收敛的精确渐近性,给出了一些更一般的新的结果.更多还原

【Abstract】 Using the martingale and empirical process methods, this dissertation in-vestigates the nonparametric statistics and limit theorems for dependent obser-vations, many results are constructed. The main contents include the following aspects.Firstly, Using the martingale method and blocking technique, the central limit theorems (CLT) for the kernel density estimator fn,K(x) and the distribu-tion function estimator Fn,K(x)=f-∞x fn,K(t)dt are established. Further,we construct a moment inequality of the conditional expectation for dependent ran-dom variables. As applications, the convergence rates of‖fn,K(x)-Efnt,Kx(x)‖p in sup-norm loss and integral Lp-norm loss are proved. Moreover, the a.s. con-vergence rates of the supremum of|fn,K(x)-E fn,K(x)|over a compact set and the whole real line are obtained. It is showed, under suitable conditions, that the optimal rates for i.i.d. random variables are also optimal for dependent ones.Secondly, using the blocking technique, we construct m dependent random variables, then we reduce the investigation for dependent samples to that for independent ones. Several limit theorems are established:First, the pointwise and the uniformly optimal weak convergence rates of the deviation of the kernel density estimator with respect to its mean (and the true density function) are derived. Moreover, the pointwise and the uniformly optimal strong convergence rates are obtained.Thirdly, we establish the CLT for wavelet regression estimator. Moreover, using empirical process methods, the upper bound for the strong expectation with respect to the entropy, Bernstein inequality and Talagrand inequality, we derive the a.s. best possible convergence rate in sup-norm loss for wavelet regression estimators.Fourthly, using the martingale method and blocking technique, we establish strong invariance principles for sums of stationary ρ-mixing random variables with finite and unbounded second moments under weaker mixing rates. Some earlier results are improved. As applications, some LIL results with finite and unbounded variance are obtained, also a conjecture raised by Shao (1993a) is solved.Finally, using the probability inequalities and the weak invariance principles, the limit behavior of the complete moment convergence of partial sums and maximal partial sums for negatively associated random variables is investigated. Some more general and new results are given.更多还原