条件中位数L_1－模核估计的逐点相合性

设（Ｘ，Ｙ）．（Ｘ１，Ｙ１），（Ｘ２，Ｙ２），…为Ｒｄ×Ｒ１上ｉ．ｉ．ｄ．随机向量序列。Ｙ对Ｘ的条件中位数θ（ｘ）定义为在Ｘ＝ｘ时Ｙ的条件分布函数的中位数．校函数Ｋ（·）是Ｒｄ上正实值函数，对ｘ∈Ｒｄ，θ（ｘ）的Ｌ１－模核估计θｎ（ｘ）定义由（１）给出．本文中，我们将文献［４］的均匀核法推广至一般核的情况，并在定义了θ的Ｌ１－模核估计基础之上，研究了其逐点相合性质．     

相依样本下条件中位数L1模核估计的相合性

本文在样本序列为同分布的φ－混合的情形下，研究了条件中位数Ｌ１－模核估计的逐点强、弱相合性。     

L_2-Cross-Validation最近邻中位数估计的相合性:m-相依情形

给出了ｍ－相依情形的Ｌ２－Ｃｒｏｓｓ－Ｖａｌｉｄａｔｉｏｎ最近邻中位数估计的弱相合性和用Ｌ_２－Ｃｒｏｓｓ－Ｖａｌｉｄａｔｉｏｎ方法选择的光滑参数的下界．     

非参数回归中位数交叉核实中位数估计的渐近性质

考虑非参数回归模型Y_ni=g(x_ni)+e_ni,1≤#em/em#≤n,其中g是定义在[0,1]上的待估计的连续函数,x_ni,1≤#em/em#≤n,是[0,1)上的固定设计点,e_ni,1≤#em/em#≤n,是中位数为0的iid随机变量,用最近邻中位数估计g_n.h(x_ni)=(m)(Y_(i(l)~(n),…,Y_(i(h))~(n))来估计g,其中h称为光滑参数。研究光滑参数的选择问题。h利用中位数交叉核实方法选择,记为h_n~*.在一定的正则性条件下,给出了h_n^*的上下界估计,估计g_n.h^*(x_ni)的收敛速度和弱一致相合性。文献中同类问题的结果只能得到平均弱相合性。     

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．     

随机窗宽核估计L1模相合性

设X_1,…,X_n是来自某个具有分布F(x)和密度f(x)的d维总体的i.i.d.样本。为估计f(x),国内外统计学家提出了许多切合实际的估计,并在其相合性方面取得了大量的成果。随机窗宽核估计就是其中之一,见文献[1—3]等等。最近Breiman L.等,Devroye L.考虑了另一类型随机窗宽核估计即:     

条件中位数L_1-模最近邻估计的逐点收敛速度

设θ(x)为Y关于X的条件中位数。本文研究了θ(x)的 L_1-模最近邻的估计的逐点收敛速度问题。得到的结果与[7]关于回归函数 E{Y|X=x}的最近邻估计的逐点收敛速度类似。     

异方差回归模型近邻型估计的相合性

本文研究异方差回归模型Ｙｉ＾（ｎ）＝ｇ（ｘｉ＾（ｎ））＋εｉ＾（ｎ）,ｉ＝１,…,ｎ,其中ｇ是右实函数,ｘｉ＾（ｎ）是非随机设计点列,εｉ＾（ｎ）是随机误差,文中定义了一类ｇ（ｘ）的近邻型估计ｇｎ（ｘ）＝（ｎ）∑（ｉ＝１）Ｗｍ（ｘ）Ｙｉ＾（ｎ）,得到了ｒ阶平均相全和渐近正态性,特别,在（∞）∑（ｎ＝１）（ｎ）∑（ｉ＝１）Ｅ／εｉ＾（ｎ）／＾ｓ／（ｎｉ）＾ｓ／ｒ〈∞,ｍａｘＥ（１≤ｉ≤ｎ）／εｉ（＾     

条件密度的近邻-核估计及其Bootstrap逼近

本文讨论了条件密度的近邻-核估计及其Bootstrap逼近的问题，证明了条件密度近邻-核估计的渐近正志性，Bootstrap逼近的相合性，讨论了有关的收敛速度．     

相依样本下条件密度递归双重核估计的强相合性

本文给出了条件密度的递归形式的双重核估计,并且在样本序列为平稳φ-混合的条件下讨论了它的强相合性。     

条件密度近邻－核估计的逼近速度

本文首先研究了条件密度函数近邻－核估计的误差分布的正态逼近精度,然后利用随机加权法构造了近邻－核估计的随机加权统计量,获得了随机加权逼近精度。     

On Consistency of the Nearest Neighbor Estimator of the Density Function and Its Applications

In this paper, we mainly study the consistency of the nearest neighbor estimator of the density function based on asymptotically almost negatively associated samples. The weak consistency,strong consistency, uniformly strong consistency and the convergence rates are established under some mild conditions. As applications, we further investigate the strong consistency and the rate of strong consistency for hazard rate function estimator.     

独立样本最近邻密度估计的强相合速度

设X，X2，…,Xn是独立同分布样本，具有共同的密度函数f(x),在f(x)满足适当的条件下给出最近邻密度估计的强相合收敛速度，其速度可达到O（n^-1/3(olgn)^1/3。     

Nearest Neighbour Searches and the Curse of Dimensionality

It is shown that efficiency of cut-off algorithms for nearestneighbour searches depends on the ratio of variance in a lowerbound space B to variance in the original space L. The usualchoice of a one dimensional B space fails for a high dimensionalL space because this ratio is then low. If the dimensionalityof B is about half that of L the equivalent of no more than75% of the full distance computations need be done, independentof the dimensionality of L space. If the variance in B spacecan be increased by either sorting or the principal componentstransformation performance is appreciably better.     

相依样本条件t分位数的核估计及其强一致收敛性

本文放宽了随机向量的独立性的要求,在假设随机向量为φ－混合的条件下,讨论了条件ｔ分位数的核估计及其强一致收敛性     

α-混合随机序列最近邻密度估计的强相合收敛速度

设{Xi）i=1^∞是一维平稳序列，具有公共的未知密度f（x），在{Xi}i=1^∞是α-混合的条件下，给出了f（x）基于前礼个观测值{Xi}i=1^∞的最近邻密度估计的强相合收敛速度，当f（x）满足适当条件，收敛速度可达到0（n^-1/3（ln n）^4（1＋p）／3））．     

The Weak Compactness of the Sets of L1(

设(     

Clustering and classification based on the L1 data depth

Clustering and classification are important tasks for the analysis of microarray gene expression data. Classification of tissue samples can be a valuable diagnostic tool for diseases such as cancer. Clustering samples or experiments may lead to the discovery of subclasses of diseases. Clustering genes can help identify groups of genes that respond similarly to a set of experimental conditions. We also need validation tools for clustering and classification. Here, we focus on the identification of outliers—units that may have been misallocated, or mislabeled, or are not representative of the classes or clusters.We present two new methods: DDclust and DDclass, for clustering and classification. These non-parametric methods are based on the intuitively simple concept of data depth. We apply the methods to several gene expression and simulated data sets. We also discuss a convenient visualization and validation tool—the relative data depth plot.     

关于L1空间多项式导数的估计

本义在加权Ｌ₁空间中对正导数多项式建立了精确的Ｂｅｒｎｓｔｃｉｎ型不等式．     

Strong Universal Pointwise Consistency of Recursive Regression Estimates

For semi-recursive and recursive kernel estimates of a regression of Y on X (d-dimensional random vector X, integrable real random variable Y), introduced by Devroye and Wagner and by Révész, respectively, strong universal pointwise consistency is shown, i.e. strong consistency P _X -almost everywhere for general distribution of (X, Y). Similar results are shown for the corresponding partitioning estimates.