L-统计量的Edgeworth展开和Bootstrap逼近

文「１」讨论了Ｌ－统计量的一种能达到０（１／√ｎ）精确性的Ｂｏｏｔｓｔｒａｐ逼近,本文则在适当条件下,证明了上述Ｂｏｏｔｓｔｒａｐ逼近能达到精确性０（１／√ｎ）,并给出了Ｌ－统计量的一阶Ｅｄｇｅｗｏｒｔｈ展开的估计。     

Von-Mises统计量的Edgeworth展开和随机加权逼近

文［１］讨论了Ｖｏｎ－Ｍｉｓｅｓ统计量的一种能达到Ｏ１ｎ的精确性的随机加权逼近，本文则给出了这种统计量的一阶Ｅｄｇｅｗｏｒｔｈ展开和一种能达到ｏ１ｎ的精确性的新的随机加权逼近．     

L-统计量的Edgeworth展开和随机加权逼近

文[1]讨论了L-统计量的一种能达到O(1√n)精确度的随机加权逼近,本文则给出了L-统计量的Edgeworth展开和一种能选到O(1√n)精确性的新的随机加权逼近．     

非参数回归函数估计的随机加权逼近

本文应用随机加权法的思想构造了回归函数的最近邻估计和核估计的随机加权统计量,并证明了用随机加权统计量的分布逼近两类估计量的分布之精度可达到ｏ（ｎ~（－１／２Ｐ）,其中１＜ｐ ≤ ２．     

L一统计量的Edgeworth展开和Bootstrap副近

文［１］讨论了Ｌ一统计量的一种能达到Ｏ（１／ｎ）精确性的Ｂｏｏｔｓｔｒａｐ副近,本文则在适当条件下,证明了上述Ｂｏｏｔｓｔｒａｐａ副近能达到精确性ｏ（１／ｎ）,并给出了Ｌ一统计量的一阶Ｅｄｇｅｗｏｒｔｈ展开的估计．     

Von-Mises统计量的Edgeworth展开和随机加权逼近

[1]讨论了Von-Mises统计量的一种能达到O（1/n的平方根）的精确性的随机加权逼近，本则给出了这种统计量的一阶Edgeworrh展开和一种能达到o（1/n的平方根）的精确性的新的随机加权逼近。     

分位点过程随机加权逼近的相合性

本文将随机加法应用于分位点过程，建立了ｎ＾１／２｛Ｆ＾－Ｄ１ｎ（ｇ）－Ｆ＾－１（Ｇ）｝的分布的随机加权逼近的相合性，并给出了其收敛速度。     

在于刀片虚拟值的广义L统计量的自动和随机加权逼近

本文基于广义Ｌ统计量的Ｊａｃｋｋｎｉｆｅ虚拟值，建立了广义Ｌ统计量的自动和随机加权逼近，证明了其相合性。     

方差估计的随机加权分布的渐近展开—非独立同分布情形

设Ｘ１，．．．，Ｘｎ是一组独立的随机变量序列，设ＥＸｉ＝０，ＶａｒＺｉ＝μ２，ｉ＝１，２，．．．，ｎ，其中μ２是待估参数，当Ｘｉ，ｉ＝１，２，．．．ｎ给定后，分别用Ｄｎ＝ｎ∑ｉ＝１Ｖｉ（Ｘｉ－Ｘ）＾２－１／ｎｎ∑ｉ＝１（Ｘｉ－Ｘ）＾２及Ｕｎ＝ｎ∑ｉ＝１（Ｘｉ－Ｘ）＾２及Ｕｎ＝ｎ∑ｉ＝１Ｖｉ（Ｘｉ－ｎ∑ｉ＝１ＶｉＸｉ）＾２－１／ｎｎ∑ｉ－１（Ｘｉ－Ｘ）＾２两种形式的随机加权分布来逼近Ｔｎ＝１／ｎｎ∑     

恰有t行含对称正元的布尔方阵的幂敛指数的估值

设Ｄｎ，２（ｔ）为恰有ｔ行含对称正元的ｎ阶布尔方阵的集合，２≤ｔ≤ｎ。本文证明了，对于任给Ａ∈Ｄｎ，２（ｔ），幂敛指数ｋ（Ａ）≤∫（ｎ－ｔ－１）＾２＋１，３ｎ－ｔ－２，当ｔ≤ｎ－［３＋√８ｎ－７／２］当ｔ〉ｎ－［３＋√８ｎ－７／２］，这里［ｘ］表示不小于ｘ的最小整数。同时，我们还证明了这个界是可以达到的，并且对Ｄｎ，２（ｔ）的极矩阵集合作了部分刻划。     

√2x^2-√2n-1的近似正态性及多总体标准差的齐性检验

计算了随机变量√2x^2的数学期望和方差,比较分析了随机变量√2x^2-√2n与√2x^2-√2n-1的近似分布的相同和不同之处,并且利用√2x^2的近似分布的正态性,建立了多总体标准差的检验法．     

密度核估计的随机加权法

利用随机加权法的思想,找出概率密度函数估计的随机加权统计量,在适当的条件下证明随机加权分布逼近核估计误差分布的精度为     

L1-Norm Estimation and Random Weighting Method in a Semiparametric Model

In this paper, the L_1-norm estimators and the random weighted statistic for a semiparametric regression model are constructed, the strong convergence rates of estimators are obtain under certain conditions, the strong efficiency of the random weighting method is shown. A simulation study is conducted to compare the L_1-norm estimator with the least square estimator in term of approximate accuracy, and simulation results are given for comparison between the random weighting method and normal approximation method.     

APPROXIMATION OF A KIND OF NEVAI-DURRMEYER OPERATORS IN Lpw SPACES

The present paper introduces a kind of Nevai-Durrmeyer operators which can be used to approximate functions in Lpw spaces with the weight w(x)=1√1-x2 and the approximate rate is also estimated.     

Jackknifed random weighting for Cox proportional hazards model

The Cox proportional hazards model is the most used statistical model in the analysis of survival time data.Recently,a random weighting method was proposed to approximate the distribution of the maximum partial likelihood estimate for the regression coefficient in the Cox model.This method was shown not as sensitive to heavy censoring as the bootstrap method in simulation studies but it may not be second-order accurate as was shown for the bootstrap approximation.In this paper,we propose an alternative random weighting method based on one-step linear jackknife pseudo values and prove the second accuracy of the proposed method.Monte Carlo simulations are also performed to evaluate the proposed method for fixed sample sizes.     

Random weighting estimation for survival function under right censorship

The random weighting method is an emerging computing method in statistics. In this paper, we propose a novel estimation of the survival function for right censored data based on the random weighting method. Under some regularity conditions, we prove the strong consistency of this estimation.     

有Edgeworth展式的分布的随机加权逼近的重对数律

本文研究了未知分布的逼近问题，利用随机加权法，给出了有Edgeworth展式的一类（未知）分布的模拟分布，证明了在一定条件下，模拟分布与未知分布的逼近精度达到O(n^-1√lnlnn），称之为随机加权逼近的重对数律。     

PP multivariate random weighting method

According to the Projection Pursuit (PP) method and the random weighting method, we propose a PP random weighting method, and set up the asymptotic distribution theory and strong limit theorem of PP random weighting empirical process. Applying this method, we obtain two kinds of goodness-of-fit test for a multivariate distribution function, i.e., we get the random weighting approximations of PP Kolmogorov Smirnov statistics (PPKS) and PP Smirnov Cramér Von Mises statistics (PPSC), we prove that the asymptotic distribution of PPKS and PPSC are the same as those of their respective random weighting approximations.Supported by the National Natural Science Foundation of China.