强混合样本回归函数估计的强相合性

本文基于强混合样本，给出回归函数核估计的强相合性，全面地改进了胡舒合（１９９５）所得的相应的初步结果。     

相依样本的经验过程振动模的收敛性

文中研究了两类重要相依样本(即φ-混合和α-混合样本)的经验过程振动模强一致收敛速度,证明了该速度与独立样本下的经验过程振动模的最优收敛速度相同.利用这些结果建立了密度函数核估计和直方图核估计的强相合性,并证明了这些强相合收敛速度达到最好速度O(n~(-1/3) log~(1/3)n)以及建立分位估计Bahadur类型的表示定理.     

φ-混合样本下密度函数的导数的核估计的一致强相合性

本文在样本为同分布Φ-混合随机变量序列的情形下研究了密度函数的导数的随机窗宽核估计的一致强相合性。     

ψ—混合误差下非参数回归函数加权核估计的相合性

本文在ψ－混合误差下讨论Ｐｒｉｅｓｔｌｅｙ，Ｍ．Ｂ．和Ｃｈａｏ，Ｍ．Ｔ．［１］提出的一类非参数回归函数加权核估计的相合性。在较弱的条件下证明了它的完全收敛性和强相合性。这些结论改进了现有的独立情形和相依情形的相应结论。     

相依样本下线性模型误差分布的相合估计

对于线性模型狔犻＝狓犻′β＋犲犻，犻＝１，…，狀，设误差序列｛犲犻｝是平稳的α 混合序列，犳（狓）为其 公共的未知密度函数，我们讨论了基于残差的犳（狓）的核估计＾犳狀（狓）＝１狀犪狀∑狀犻＝１犓（＾犲狀犻－狓犪狀）的弱相合性、逐点强相合性、一致强相合性及其收敛速度，其中＾犲狀犻为Ｌ．Ｓ．估计的残差．     

混合序列矩不等式和非参数估计

对p-混合、(?)-混合序列给出两个矩不等式,它们在加权和序列研究中比邵启满在[6]、[7]中给出的矩不等式更实用.作为应用,这里讨论非参数递归密度核估计的强收敛速度和非参数回归函数加权核估计的强相合性,获得较好结论.     

￠－混合误差下非参数回归函数加权核估计的相合性

本文在－混合误差下讨论Ｐｒｉｅｓｔｌｅｙ，Ｍ．Ｂ．和Ｃｈａｏ，Ｍ．Ｔ［１］提出的一类非参数回归函数加权核估计的相合性。在较弱的条件下证明了它的完全收敛性和强相合性。这些结论改进了现有的独立情形和相依情形的相应结论。     

ψ—混合样本核密度估计的强一致相合性

本文研究了基于φ-混合样本且核为可变化的密度函数估计的强一致相合性,获得了与独立同分布样本时同样优良的收敛速度O((n/logn)~(-1/3)).     

球面数据核近邻估计的强相合性

该文绘出了球面数据密度函数的核近邻估计,通过对核估计与近邻估计相互关系的讨论,建立了核近邻估计的逐点强相合性及一致强相合性．     

完全与截尾样本时回归函数的核估计

本文得到了完全与截尾样本时回归函数核估计的强相合性．接着，构造了截尾样本的改良核估计，在Ｅ｜Ｙ｜＜∞下，得到了其强相合性．     

A note on the universal consistency of the kernel distribution function estimator

The problem of universal consistency of data driven bandwidth selectors for the kernel distribution estimator is analyzed. We provide a uniform in bandwidth result for the kernel estimate of a continuous distribution function. Our smoothness assumption is minimal in the sense that if the true distribution function has some discontinuity then the kernel estimate is no longer consistent.     

EDGEWORTH EXPANSION FOR NEAREST NEIGHBOR- KERNEL ESTIMATE AND RANDOM WEIGHTING APPROXIMATION OF CONDITIONAL DENSITY

§ 1　 IntroductionThere are many discussions on the estimate of density,for example,the kernel andnearest neighbor estimates.Reference [1 ] gave the nearest neighbor-kernel estimate ofconditional density.[2 ] discussed the properties on the nearest neighbor-kernel estimateand bootstrap approximation of conditional density.[3 ] studied some asymptotical proper-ties on kernel estimate and random weighting approximation of density.In this paper,wewill discuss the properties of the nearest neighb…     

BIVARIATE ESTIMATION WITH LEFT-TRUNCATED DATA

1. Introductionffendomly truncated data frequently arise in medical studies; other application areas include economics, insurance and astronomy in a broad senses random truncation correspondsto biased sampling, where only partial or incomplete data are aVailable about the variableof interest. A typical realization. can occur as follows: Suppose that individuals/items experience tWO consecutive events in time, an initiating eveal at t and a terminating eveal ats. Usuajly, statistical niterest …     

Asymptotic Properties of a Nonparametric Regression Function Estimator with Randomly Truncated Data

In this paper, we define a new kernel estimator of the regression function under a left truncation model. We establish the pointwise and uniform strong consistency over a compact set and give a rate of convergence of the estimate. The pointwise asymptotic normality of the estimate is also given. Some simulations are given to show the asymptotic behavior of the estimate in different cases. The distribution function and the covariable’s density are also estimated.     

Strong consistency of automatic kernel regression estimates

Regression function estimation from independent and identically distributed bounded data is considered. TheL ₂ error with integration with respect to the design measure is used as an error criterion. It is shown that the kernel regression estimate with an arbitrary random bandwidth is weakly and strongly consistent forall distributions whenever the random bandwidth is chosen from some deterministic interval whose upper and lower bounds satisfy the usual conditions used to prove consistency of the kernel estimate for deterministic bandwidths. Choosing discrete bandwidths by cross-validation allows to weaken the conditions on the bandwidths. Research supported by DAAD, NSERC and Alexander von Humboldt Foundation. The research of the second author was completed during his stay at the Technical University of Szczecin, Poland.     

Some inequalities for strong mixing random variables with applications to density estimation

In this paper, we establish an inequality of the characteristic functions for strongly mixing random vectors, by which, an upper bound is provided for the supremum of the absolute value of the difference of two multivariate probability density functions based on strongly mixing random vectors. As its application, we consider the consistency and asymptotic normality of a kernel estimate of a density function under strong mixing. Our results generalize some known results in the literature.     

Spatial Nonparametric Regression Estimation: Non-isotropic Case

Data collected on the surface of the earth often has spatial interaction. In this paper, a non-isotropic mixing spatial data process is introduced, and under such a spatial structure a nonparametric kernel method is suggested to estimate a spatial conditional regression. Under mild regularities, sufficient conditions are derived to ensure the weak consistency as well as the convergence rates for the kernel estimator. Of interest are the following: (1) All the conditions imposed on the mixing coefficient and the bandwidth are simple; (2) Differently from the time series setting, the bandwidth is found to be dependent on the dimension of the site in space as well; (3) For weak consistency, the mixing coefficient is allowed to be unsummable and the tendency of sample size to infinity may be in different manners along different direction in space; (4) However, to have an optimal convergence rate, faster decreasing rates of mixing coefficient and the tendency of sample size to infinity along each direction a     

RCV-based error density estimation in the ultrahigh dimensional additive model

In this paper, we mainly study how to estimate the error density in the ultrahigh dimensional sparse additive model, where the number of variables is larger than the sample size. First, a smoothing method based on B-splines is applied to the estimation of regression functions. Second, an improved two-stage refitted cross-validation (RCV) procedure by random splitting technique is used to obtain the residuals of the model, and then the residual-based kernel method is applied to estimate the error density function. Under suitable sparse conditions, the large sample properties of the estimator including the weak and strong consistency, as well as normality and the law of the iterated logarithm are obtained. Especially, the relationship between the sparsity and the convergence rate of the kernel density estimator is given. The methodology is illustrated by simulations and a real data example, which suggests that the proposed method performs well.

     

混合互补判断矩阵一致性研究

给出混合互补判断矩阵一致性的定义和判别加性一致性的方法.定义了核算子、核矩阵,对带有精确数、三角模糊数和梯形模糊数的混合互补判断矩阵给出基于核矩阵的一致性调整方法,调整量可以是精确数也可以是模糊数.最后给出一个应用实例.