随机删失下乘积限过程和累积失效率过程振动模的局部渐近性

在随机删失下研究了乘积限过程和累积失效率过程的振动模的局部性质 .给出了这两个过程的振动模的重对数律 ,并应用这些结果得到了几种核密度估计和Bahadur-Kiefer过程的精确收敛速度     

失效率的一种截尾非参数估计

本文构造了失效率的一种截尾非参数估计,并给出了它的均方收敛速度和强相合的局部一致收敛速度。     

NA样本最近邻密度估计的相合性

在NA样本下研究最近邻密度估计的相合性，给出弱相合性、强相合性、一致强相合性以及它们的收敛速度的充分条件．同时研究了失效率函数估计的一致强相合性     

失效率的一种截尾非参数估计及其均方收敛和强相合速度

本文在截尾样本下构造了失效率的一种截尾非参数估计，并给出了其均方收敛及强相合性的局部一致收敛速度。     

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.     

一种近邻密度估计的强收敛速度

本文讨论了俞军（１９８６）提出的一种近邻密度估计的逐点强收敛速度和一致强收敛速度．并证明了收敛速度的主阶部分不能达到．     

最近核邻型的光滑条件分位过程强逼近及其应用

文中证明了核邻型的光滑条件分位过程的强逼近，获得了其一致逼近速度．并由此结果推导出了光滑条件分位估计的渐近正态性、弱收敛和对数律等深刻结果．     

随机右删失下密度与危险率函数的估计及其收敛速度

本文根据定义的密度函数的估计式，得到了其一致收敛速度，并由此得到了危险率函数的强一致收敛速度．     

随机截断数据下乘积限过程的振动模

文中研究了随机截断数据下的剩积限过程的振动行为，证明了其振动模的收敛速度与完全样本下经验过程振动模的收敛速度相一致。     

混合误差下回归函数小波估计的一致收敛速度

该文构造了回归函数的一类小波估计，在误差序列为ψ 混合或φ 混合下得到了小波估计的强一致收敛速度和狉阶矩一致收敛速度．     

Strong limit theorems for oscillation moduli of PL-process and cumulative hazard process under truncation and censorship with applications

In this paper we obtain exact rates of uniform convergence for oscillation moduli and Lipschitz-1/2 moduli of PL-process and cumulative hazard process when the data are subject to left truncation and right censorship. Based on these results, the exact rates of uniform convergence for various types of density and hazard function estimators are derived. Research supported by the Postdoctoral Programme Foundation and the National Natural Science Foundation of China     

Some properties of bivariate empirical hazard processes under random censoring

In Campbell (1982, IMS Lecture Notes—Monograph Series Vol. 2, pp. 243–256, IMS, Hayward, CA) and Campbell and Földes (1982, Proceedings, Internat. Colloq. Nonparametric Statist. Inform., 1980, North-Holland, New York) some asymptotic properties of bivariate empirical hazard processes under random censoring are given. Taking the representation of the empirical hazard process for bivariate randomly censored samples in Campbell, op. cit., as a starting point and restricting attention to strong properties, we obtain a speed of strong convergence for the weighted bivariate empirical hazard processes as well as a speed of strong uniform convergence for bivariate hazard rate estimators. Our approach is based on a local fluctuation inequality for the bivariate hazard process and differs from the martingale methods quite often used in the univariate case.     

The almost sure behavior of the oscillation modulus for PL-process and cumulative hazard process under random censorship

The local behavior of oscillation modulus of the product-limit (PL) process and the cumulative hazard process is investigated when the data are subjected to random censoring. Laws of the iterated logarithm of local oscillation modulus for the PL-process and the cumulative hazard process are established. Some of these results are applied to obtain the almost sure best rates of convergence for various types of density estimators as well as the Bahadur-Kiefer type process. Project supported in part by the National Natural Science Foundation of China (Grant No. 19701037).     

Best approximation and moduli of smoothness: Computation and equivalence theorems

In this paper we investigate the trigonometric series with the β-general monotone coefficients. First, we study the uniform convergence criterion. The estimates of best approximations and moduli of smoothness of the series in uniform metrics are obtained in terms of coefficients. These results imply several important relations between moduli of smoothness of different orders (in particular, Marchaud-type inequality) and best approximations.     

Nonparametric Estimation for Semi-Markov Processes Based on its Hazard Rate Functions

The problem of estimating the Markov renewal matrix and the semi-Markov transition matrix based on a history of a finite semi-Markov process censored at time T (fixed) is addressed for the first time. Their asymptotic properties are studied. We begin by the definition of the transition rate of this process and propose a maximum likelihood estimator for the hazard rate functions and then we show that this estimator is uniformly strongly consistent and converges weakly to a normal random variable. We construct a new estimator for an absolute continous semi-Markov kernel and give detailed derivation of uniform strong consistency and weak convergence of this estimator as the censored time tends to infinity. This revised version was published online in June 2006 with corrections to the Cover Date.     

A rate of convergence for asymptotic contractions

In [P. Gerhardy, A quantitative version of Kirk's fixed point theorem for asymptotic contractions, J. Math. Anal. Appl. 316 (2006) 339-345], P. Gerhardy gives a quantitative version of Kirk's fixed point theorem for asymptotic contractions. This involves modifying the definition of an asymptotic contraction, subsuming the old definition under the new one, and giving a bound, expressed in the relevant moduli and a bound on the Picard iteration sequence, on how far one must go in the iteration sequence to at least once get close to the fixed point. However, since the convergence to the fixed point needs not be monotone, this theorem does not provide a full rate of convergence. We here give an explicit rate of convergence for the iteration sequence, expressed in the relevant moduli and a bound on the sequence. We furthermore give a characterization of asymptotic contractions on bounded, complete metric spaces, showing that they are exactly the mappings for which every Picard iteration sequence converges to the same point with a rate of convergence which is uniform in the starting point.     

Convergence of densities of some functionals of Gaussian processes

The aim of this paper is to establish the uniform convergence of the densities of a sequence of random variables, which are functionals of an underlying Gaussian process, to a normal density. Precise estimates for the uniform distance are derived by using the techniques of Malliavin calculus, combined with Stein?s method for normal approximation. We need to assume some non-degeneracy conditions. First, the study is focused on random variables in a fixed Wiener chaos, and later, the results are extended to the uniform convergence of the derivatives of the densities and to the case of random vectors in some fixed chaos, which are uniformly non-degenerate in the sense of Malliavin calculus. Explicit upper bounds for the uniform norm are obtained for random variables in the second Wiener chaos, and an application to the convergence of densities of the least square estimator for the drift parameter in Ornstein–Uhlenbeck processes is discussed.     

A sharp error estimate for the numerical solution of multivariate Dirichlet problems

For the multidimensional Dirichlet problem of the Poisson equation on an arbitrary compact domain, this study examines convergence properties with rates of approximate solutions, obtained by a standard difference scheme over inscribed uniform grids. Sharp quantitative estimates are given by the use of second moduli of continuity of the second single partial derivatives of the exact solution. This is achieved by employing the probabilistic method of simple random walk.     

Uniform Convergence of Wavelet Expansions of Gaussian Random Processes

New results on uniform convergence in probability for the most general classes of wavelet expansions of stationary Gaussian random processes are given.     

Hazard Rate Estimation in Nonparametric Regression with Censored Data

Consider a regression model in which the responses are subject to random right censoring. In this model, Beran studied the nonparametric estimation of the conditional cumulative hazard function and the corresponding cumulative distribution function. The main idea is to use smoothing in the covariates. Here we study asymptotic properties of the corresponding hazard function estimator obtained by convolution smoothing of Beran's cumulative hazard estimator. We establish asymptotic expressions for the bias and the variance of the estimator, which together with an asymptotic representation lead to a weak convergence result. Also, the uniform strong consistency of the estimator is obtained.