左截断右删失数据下分位密度估计的重对数律

在左截断右删失数据下，我们基于乘积限估计给出了分位密度估计， 获得了分位密度估计及其导数的重对数律。     

乘积极限估计的重对数律

利用右删失数据估计寿命分布时,常用乘积极限估计.本文给出乘积极限估计是均匀强相合估计的充要条件.证明乘积极限估计的均匀收敛的重对数律.     

基于截断删失数据乘积限估计的一些逼近定理

本文证明了由Ｔｓａｉ等［２］所定义的基于截断删失数据乘积限估计可被一Ｕ—统计量以足够的精度逼近，以至由该Ｕ—统计量的ＢｅｒｙＥｓｅｎ定理可得此乘积限估计的ＢｅｒｙＥｓｓｅｎ不等式．此外，应用Ｇｉｂｊｅｌｓ等人［１，定理１（Ｃ）］的有关结果，得到了此处乘积限估计误差的ｒ（＞１）阶绝对矩不等式和泛函型重对数律     

正则和函数的强极限及应用

研究了随机变量序列正则和函数的重对数律,作为应用,给出了正的随机变量序列部分和乘积的重对数律.同时,还获得了独立随机变量阵列相应的单对数律结果.     

广义矩估计的重对数律

本文研究了广义矩估计的性质.利用强相合性的条件,得到了广义矩估计满足重对数律的结果.     

广义矩估计的重对数律（英文）

本文研究了广义矩估计的性质.利用强相合性的条件,得到了广义矩估计满足重对数律的结果.     

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     

正相协列乘积和的重对数律与强大数律

证明了强平稳正相协列乘积和的重对数律与不同分布正相协列乘积和的强大数律,指出了部分和服从强大数律但乘积和未必服从强大数律这一事实,并讨论了定理2中一个条件的必要性．     

分位点函数的光滑非参数估计的BAHADUR表示

文中对分位函数给出了具有更广泛应用的光滑分位估计,证明了该光滑分位估计的逐点和一致的Bahadur强表示定理;并由此结果推导了估计的重对数律,强逼近等深刻结果。     

分组数据情形下对数正态分布参数的最大似然估计

我们研究了分组数据情形下对数正态分布所含参数的最大似然估计存在且唯一的充要条件，进而得到了最大似然估计具有强相合性及收敛速度服从重对数律的结论。     

Quantile process for left truncated and right censored data

In this paper, we consider the product-limit quantile estimator of an unknown quantile function when the data are subject to random left truncation and right censorship. This is a parallel problem to the estimation of the unknown distribution function by the product-limit estimator under the same model. Simultaneous strong Gaussian approximations of the product-limit process and product-limit quantile process are constructed with rate . A functional law of the iterated logarithm for the maximal deviation of the estimator from the estimand is derived from the construction. Work partially supported by NSC Grant 89-2118-M-259-011.     

A Strong Representation of the Product-Limit Estimator for Left Truncated and Right Censored Data

In this paper we consider the TJW product-limit estimatorF_n(x) of an unknown distribution functionFwhen the data are subject to random left truncation and right censorship. An almost sure representation of PL-estimatorF_n(x) is derived with an improved error bound under some weaker assumptions. We obtain the strong approximation ofF_n(x)−F(x) by Gaussian processes and the functional law of the iterated logarithm is proved for maximal derivation of the product-limit estimator toF. A sharp rate of convergence theorem concerning the smoothed TJW product-limit estimator is obtained. Asymptotic properties of kernel estimators of density function based on TJW product-limit estimator is given.     

STRONG CONVERGENCE RATES OF SEVERAL ESTIMATORS IN SEMIPARAMETRIC VARYING-COEFFICIENT PARTIALLY LINEAR MODELS

This article is concerned with the estimating problem of semiparametric varyingcoefficient partially linear regression models. By combining the local polynomial and least squares procedures Fan and Huang （2005） proposed a profile least squares estimator for the parametric component and established its asymptotic normality. We further show that the profile least squares estimator can achieve the law of iterated logarithm. Moreover, we study the estimators of the functions characterizing the non-linear part as well as the error variance. The strong convergence rate and the law of iterated logarithm are derived for them, respectively.     

删失数据的光滑乘积限估计的渐近性质

在删失数据的模型下，对于光滑未知的分布函数Ｆ０，文中提出了光滑化的方法去估计Ｆ０，得到了光滑ＰＬ估计Ｆｎ，并建立了Ｆｎ在Ｄ（－∞，Ｔ），Ｔ＜ＴＦ上的弱收敛和强相合的结果．同时也获得了光滑ＰＬ过程的强逼近和重对数律．     

删失数据平滑非参数分位估计

文中在随机右删失意义下，对于未知分布函数的分位点，基于ＰＬ估计给出了一种平滑的非参数核分位估计，推导出了该估计的逐点和一致强弱Ｂａｈａｄｕｒ类型表示定理，并由此结果获得了平滑分位计的渐近正态性及重对数律等深刻结果。     

Sur l'estimateur des moindres carrés d'un modèle autorégressif fonctionnel

Assuming the stability of a nonlinear autoregressive process, we give simple conditions ensuring strong consistency, asymptotic normality and a law of the iterated logarithm for the least squares estimator. The last result yields an almost sure identification of the true model using a suitably penalized contrast.     

On Least Squares Estimation for Stable Nonlinear AR Processes

Following a Markov chain approach, this paper establishes asymptotic properties of the least squares estimator in nonlinear autoregressive (NAR) models. Based on conditions ensuring the stability of the model and allowing the use of a strong law of large number for a wide class of functions, our approach improves some known results on strong consistency and asymptotic normality of the estimator. The exact convergence rate is established by a law of the iterated logarithm. Based on this law and a generalized Akaike's information criterion, we build a strongly consistent procedure for selection of NAR models. Detailed results are given for familiar nonlinear AR models like exponential AR models, threshold models or multilayer feedforward perceptions.     

A LAW OF THE ITERATED LOGARITHM FOR NEIGHBOR-TYPE REGRESSION FUNCTION ESTIMATOR

Let (X, Y) be a two-dimensional random variable. A law of the iterated logarithm is established for a smoothed neighbor-typo estimator of the regression function m(x)=E(Y|X=x) under conditions much weaker than needed for the Nadaraya-Watson estimator. Also the sharp pointwise rates of strong consistency of this estimator is discussed in detail.     

The precise asymptotic behavior of parameter estimators in Ornstein-Uhlenbeck process

In the present paper, we study the asymptotic behavior for estimator of the drift parameter in an Ornstein-Uhlenbeck process. The Lr-convergence rate and the precise asymptotics in the law of iterated logarithm and in the law of logarithm for the estimator are obtained. Moreover, we also get the complete moment convergence of this estimator. The main method of this paper is the deviation inequality for the quadratic functional.