随机PP Kolmogorov多维拟合优度检验乃其Bootstrap逼近

本文提出了一种基于随机选择投影方向的PP型棉球等高分布族的拟合优度检验，其特点是计算上较通常的PP检验统计量简单．得到了其检验统计量在零假设下的极限分布，讨论了其Bootstrap逼近及逼近的相容性．     

尺度参数变点的非参数检验

本文讨论了尺度参数模型参数变点的假设检验问题\bd 基于两样本$U$\,-统计量, 我们给出了两个检验, 并且研究了检验统计量分布的极限性质\bd 我们证明了这两个检验统计量的极限分布分别是$\sup\limits_{0相似文献   

两类探索性投影寻踪指标的渐近性

本文得到了原假设条件下PP Friedman指标的渐近分布及自助法逼近。当数据有非线性结构时,本文导出了PP Friedman指标及PP Neyman指标其最优方向和最优投影指标值的渐近分布以及自助法逼近;特别,给出了最优方向及最优投影指标值的自助法置信集。     

位置参数变点的非参数检验及其渐近性质

本文基于U-统计量,对于位置参数模型,讨论了位置参数变点的检验问题,给出了检验统计量并研究它的分布的极限性质,证明了检验统计量的极限分布是sup |B(t)|,其中{B(t),0＜t＜1}是一个Brown桥.将此结果应用到了双参数指数分布和Weibu11分布尺度参数变点的检验问题中.     

极值分布和威布尔分布异常数据的检验方法

本文对威布尔分布的极值分布异常数据的检验给出了一系列的方法，首先，导入了极值分布下一般Ｄｉｘｏｎ型统计量的精确分布，同时还给出了改进的Ｇ型统计量，及它们的分位点表。最后本文提出了一个新的统计量；Ｆ型统计量，并用Ｍｏｎｔｅ－Ｃａｒｌｏ模拟的方法给出其分位点表，从而首次给出威布尔分布异常值的直接检验方法。本文进一步讨论了这些检验方法的功效，且表明Ｆ型检验是最优的。     

复合零假设下的修正Berk-Jones检验

首先给出带估计参数的上界型和积分型两种修正Berk-Jones拟合优度检验统计量,接着在复合零假设下研究了这两种检验统计量的渐近分布.所得结论可以为进一步讨论非参数似然比拟合优度检验提供较好的理论基础.     

基于Beta分布形状的拟合优度检验

本文指出了拟合优度检验中选用经验频率公式时存在的误区,对顺序统计量失效概率分布进行了偏态和峰态分析,提出了一个基于Beta分布形状特征的经验分布函数,给出了精确值和近似值两种计算方法,在此基础上建立新的极值型检验统计量．利用Monte Carlo方法进行数值模拟,得到0．01、0．05、0．1显著度水平下检验统计量的临界值,并利用常用的分布模型进行检验功效比较,数值模拟结果表明基于分布形状的经验频率公式能更好地反映顺序统计量失效概率分布的集中趋势,证明了本文提出的两种检验统计量在中小样本条件下具有更优的检验功效．     

一类上界型拟合优度检验统计量的精确分布

对于简单假设的拟合优度检验,Zhang (2002)构造出一类上界型检验.取不同的参数$\lambda$和不同的权函数$q(t)$,这类检验包含了Kolmogorov-Smirov检验, Berk and Jones(1979)检验等已有的上界型检验.文献中仅对极少数$\lambda$和$q(t)$所对应的检验给出了零假设下的精确分布.然而, 针对不同的问题, ``好'的检验是不同的,因此有必要对任意给定的$\lambda$和$q(t)$情况, 讨论该类检验.本文对任意给定的$\lambda$和$q(t)\equiv 1$情况,导出了相应上界型检验统计量在零假设下的精确分布. 当样本容量$n$较大时,精确分布的计算时间较长, 本文还通过模拟比较得到了在不同样本量下,应采用的计算方法. 最后, 给出一个实际例子对前述方法加以简单说明.     

异常数据检验的屏蔽效应

本文对屏蔽效应的一系列定理进行了归纳和证明，针对极值分布或威布尔分布，讨论了文献[1]中提出的六种检验统计量的屏蔽效应，文中仅给出了对Dixon型和F型统计量的结果，说明F型统计量能避免屏蔽效应。     

空间误差模型的多个异常值得分检验

本文研究了空间误差模型(SEM)中多个异常值的检验问题,基于均值漂移模型和方差加权模型这两种异常值模型给出了得分检验统计量的具体形式及其渐近分布。并应用实例分析验证了检验统计量的有效性,最后给出了修正模型的方法。     

BOOTSTRAPPING GENERALIZED U－PROCESSES AND V－PROCESSES AND THEIR APPLICATIONS IN PROJECTION PURSUIT

ＢＯＯＴＳＴＲＡＰＰＩＮＧＧＥＮＥＲＡＬＩＺＥＤＵ－ＰＲＯＣＥＳＳＥＳＡＮＤＶ－ＰＲＯＣＥＳＳＥＳＡＮＤＴＨＥＩＲＡＰＰＬＩＣＡＴＩＯＮＳＩＮＰＲＯＪＥＣＴＩＯＮＰＵＲＳＵＩＴ￥ＺＨＡＮＧＤＩＸＩＮ（张涤新）（ＤｅｐａｒｔｍｅｎｔｏｆＳｔａｔｉｓｔｉ...     

Bootstrapping generalizedU-processes andV-processes and their applications in projection pursuit

In this paper, we study bootstrap approximation for generalizedU-processes (GUP) indexed by a class of functions. Under mild conditions we obtain that the asymptotic distributions of bootstrapping generalizedU-processes (BGUP) are the same as those of GUP almost surely. As a result, the asymptotic properties of bootstrap approximation for PP generalizedU-processes (BPPGUP) are obtained. In addition we have derived bootstrap approximation for generalizedV-processes (GVP). Thus, we can use BGUP or bootstrapping GVP (BGVP) to simulate GUP and GVP.This project is supported by the National Natural Science Foundation of China and the Science Foundation of Educational Committee of Guizhou.     

对Bootstrap方法的自助抽样的改进

Bootstrap方法是一种常用的统计推断方法,它的无先验性,以及计算过程中只需要实际的观测数据,使得其可方便的应用于实际的数据处理之中。对连续情形而言,Bootstrap本身的计算特性限制了自助样本的生成范围,即只能从原样本再采样,从而使得自助分布产生偏离,无法渐近于真实情形。鉴于此不足,本文提出了改进的Bootstrap方法,它通过对自助样本生成范围的拓展,克服了这一不足,获得了更加精确的参数估计。     

高维随机截尾数据的PP拟合优度检验

关于一维删截数据的拟合优度检验，已有相当多的文献，但高维截尾数据的拟合优度检验尚不多见．本文用PP技巧讨论了高维截尾数据的拟合优度检验，得到了检验统计量的渐近分布，并讨论了其Bootstrap逼近及逼近的相容性和检验的渐近功效．     

删截分布未知的多维随机删截数据的PP拟合优度检验

文[5]在假定删截分布已知的条件下,用投影寻踪（PP）技巧讨论了多维随机删截数据的PP拟合优度检验问题．本文讨论截尾分布未知时,多维随机删截数据的拟合优度检验问题,得到了检验统计量在零假设成立时的渐近分布,并讨论了其Bootstrap逼近．     

线性相关模型中误差方差的经验似然估计及其Bootstrap

该文利用经验似然方法，对线性相关模型中误差方差的传统最小二乘型估计进行修正，得到的修正估计其渐近方差比传统估计的更小．同时，我们还讨论了修正估计的Bootstrap逼近问题．关键词##4相关模型;;误差方差;;最小二乘;;经验似然;;Bootstrap．     

Bootstrapping the change-point of a hazard rate

This paper concerns the asymptotic validity of the bootstrap method in a non-regular model. Specifically, it is shown that the parametric bootstrap of the change-point parameter in the change-point hazard rate model works.     

具有多合同的广义加权保费的信度估计(英文)

本文讨论了广义加权保费原理下的信度估计,并把结论推广到多合同模型.通过概率分布的变换,本文得到了多合同模型下广义加权保费的非齐次和齐次信度估计.并且讨论了这些估计的统计性质.最后,运用重抽样方法讨论了信度因子中未知结构参数的估计.数值模拟表明,非齐次信度估计能运用于保险实际.     

On a mapping approach to investigating the bootstrap accuracy

Summary. A simple mapping approach is proposed to study the bootstrap accuracy in a rather general setting. It is demonstrated that the bootstrap accuracy can be obtained through this method for a broad class of statistics to which the commonly used Edgeworth expansion approach may not be successfully applied. We then consider some examples to illustrate how this approach may be used to find the bootstrap accuracy and show the advantage of the bootstrap approximation over the Gaussian approximation. For the multivariate Kolmogorov–Smirnov statistic, we show the error of bootstrap approximation is as small as that of the Gaussian approximation. For the multivariate kernel type density estimate, we obtain an order of the bootstrap error which is smaller than the order of the error of the Gaussian approximation given in Rio (1994). We also consider an application of the bootstrap accuracy for empirical process to that for the copula process. Received: 23 June 1995 / In revised form: 18 June 1996     

An evaluation of the multivariate dispersion charts with estimated parameters under non‐normality

Various charts such as |S|, W, and G are used for monitoring process dispersion. Most of these charts are based on the normality assumption, while exact distribution of the control statistic is unknown, and thus limiting distribution of control statistic is employed which is applicable for large sample sizes. In practice, the normality assumption of distribution might be violated, while it is not always possible to collect large sample size. Furthermore, to use control charts in practice, the in‐control state usually has to be estimated. Such estimation has a negative effect on the performance of control chart. Non‐parametric bootstrap control charts can be considered as an alternative when the distribution is unknown or a collection of large sample size is not possible or the process parameters are estimated from a Phase I data set. In this paper, non‐parametric bootstrap multivariate control charts |S|, W, and G are introduced, and their performances are compared against Shewhart‐type control charts. The proposed method is based on bootstrapping the data used for estimating the in‐control state. Simulation results show satisfactory performance for the bootstrap control charts. Ultimately, the proposed control charts are applied to a real case study.