可变样本容量的质量控制图

本文根据Ｃｏｓｔａ的可变样本容量的ｘ＾－控制图的模型设计具有可变样本容量在ｘ＾～和Ｒ图,计算了在可变样本容量（ＶＳＳ）下发信号前的平均样本数和平均时间,并同固定样本容量（ＦＳＳ）和可变抽样区间（ＶＳＩ）的ｘ＾～和Ｒ图作比较。     

给定抽样调查费用时最优轮换率及样本容量的确定

建立了前期和现期总的调查规模即样本容量不一定相等时的样本轮换模型,并求出了给定费用时的最优样本容量及最优轮换率,并分析了3种特殊情况,其中第3种特殊情况正是[1,2]中的有关结果.     

具有可变样本容量的非正态EWMA控制图

为了提高控制图的监控效率,本文研究非正态分布下,EWMA控制图的可变样本容量设计问题。首先利用Burr分布近似各种非正态分布,构造可变样本容量的非正态EWMA控制图;其次运用马尓科夫链法计算可变样本容量非正态EWMA控制图的平均运行长度;然后与传统的非正态EWMA控制图进行比较得出:当过程中出现小波动时,可变样本容量的非正态EWMA控制图能够更快地发现过程中的异常波动,具有较小的平均运行长度,其监控效率明显优于传统的非正态EWMA控制图。     

一种产品链的认定方法及应用

以统计理论为依据 ,应用产品关联原理 ,建立了产品链关联度模型、关联度检验模型、关联力模型及其搜索模型 ,在进行关联度检验时 ,通过将时序数据和截面数据结合 ,扩大样本容量 ,使样本容量达到检验模型要求 ,并应用所建立的模型对宜昌市产品链进行认定 .     

例谈用样本估计总体的解题思维策略

样本估计总体的教材内容似乎很容易弄懂,但是在实际的学习中,许多同学在整体把握概念上有欠火候,往往误解、曲解一些概念的内涵,本文略举数例,帮助同学们辨析概念和解题思路,学好本节内容.例1在用样本估计总体的过程中,下列说法正确的是()(A)总体容量越大,估计越精确.(B)总体容量越小,估计越精确.(C)样本容量越大,估计越精确.(D)样本容量越小,估计越精确.解既然是用样本估计总体,那么总体容量就不是我们所要考虑的,因此只能让样本容量尽可能地大,故答案选(C).评注用样本估计总体是研究统计问题的一个基本思想方法,一般地样本容量越大,这种…     

基于样本协方差矩阵的多维随机数生成方法

对于概率模型未知的多维数据样本容量扩充问题,根据主成分分析原理以及多维正态分布的性质,讨论并给出了与已知多维样本数据有相同协方差结构的模拟数据生成算法,并在此基础上给出了变量的离散化处理方法。实现了在小样本数据基础上不改变变量间协方差结构的样本容量扩充,为小样本条件下的数学建模、检验和分析提供样本数据支撑。     

指数分布串—并联混合系统产品的统计分析

给出单元寿命服从同一指数分布的串-并联混合系统产品参数的矩估计和极大似然估计,并通过大量Monte-Carlo模拟比较了估计的精度,得到在样本容量小于35时矩估计优于极大似然估计,而样本容量不小于35时极大似然估计优于矩估计.另外,还给出了参数的精确区间估计与近似区间估计,并通过大量Monte-Carlo模拟考察了区间估计的精度.     

计数序贯抽样检验方法

计数一次抽检方案(n,c),其中样本容量n为一固定的数。使用这种抽检方法并没有利用样本所提供的全部信息,一直要等到取完n件产品后才开始作判断。实际上,有时当　取到k(k相似文献   

两样本队列半数生存期检验:反射和检基统计量

依据反射或检基原理，本文提出用于截尾资料的两样本队列半数生存期（ＣＨＬ）检验．两样本合并ＣＨＬ经指数内插取自Ｋａｐｌａｎ－Ｍｅｉｅｒ或Ｂｅｒｋｓｏｎ－Ｇａｇｅ估计值．连续性校正，经以有效样本容量取代样本容量，扩展自Ｙａｔｅｓ校正．合并标准误来自同源性生存率方差估计值，后者经有效样本容量扩展自二项分布方差．无截尾时，这些统计量还原为经典中位数检验．与反射统计量相比，检基统计量具有更高的功效．附有工作实例描述其临床应用．     

第五部分 统计初步复习研究

【复习目标】 了解总体、个体、样本、样本容量等概念及样本方差和标准差的意义;理解众数、中位数、总体平均数、样本平均数、加权平均数的意义;能指出研究对象的总体、个体、样本及样本容量,掌握众数、中位数的求法及平均数、加权平均数的计算公式,会计算样     

截尾分布与截尾样本的统计分析

[1]中给出了样本容量n已知的(n,r）定时截尾不完全样本的分析方法。在[2]中我们讨论了n未知的（n,r）定时截尾不完全样本并较好地处理了晚截尾的接近完全样本的分析。本文中我们相应于截尾样本，引入截尾分布，通过用截尾样本拟合截尾分布而得到寿命T的整体分布。此方法无论对n已知还是n未知，对早截尾还是晚截尾的（n,r）定时截尾样本都适用。并且此方法还可推广到对其它形式的不完全样本的处理。     

Sample selection procedure in daily trading volume processes

In this paper, we propose a procedure of selecting samples from a set of samples coming from Markovian processes of finite order and finite alphabet. Under the assumption of the existence of a law that prevails in at least q% of the samples of the collection, we show that the procedure allows to identify samples governed by the predominant law. The approach is based on a local metric between samples, which tends to zero when we compare samples of identical law and tends to infinity when comparing samples with different laws. The local metric allows to define a criterion which takes arbitrarily large values when the previous assumption about the existence of a predominant law does not hold. By means of this procedure, we map similarities and dissimilarities of some Brazilian stocks' daily trading volume dynamic.     

END样本最近邻密度估计的强相合速度

本文研究了扩展负相依(END)样本最近邻密度估计的强相合性问题.利用END序列的Bernstein型不等式和截尾的方法,获得了END样本最近邻密度估计的强相合速度,推广了NA样本和ND样本最近邻密度估计的相应结果.     

Regularized least square regression with dependent samples

In this paper we study the learning performance of regularized least square regression with α-mixing and ϕ-mixing inputs. The capacity independent error bounds and learning rates are derived by means of an integral operator technique. Even for independent samples our learning rates improve those in the literature. The results are sharp in the sense that when the mixing conditions are strong enough the rates are shown to be close to or the same as those for learning with independent samples. They also reveal interesting phenomena of learning with dependent samples: (i) dependent samples contain less information and lead to worse error bounds than independent samples; (ii) the influence of the dependence between samples to the learning process decreases as the smoothness of the target function increases.     

A comment on “empirical Bayes estimation of the truncation parameter with asymmetric loss function using NA samples”

Recently, there are some empirical Bayes procedures using NA samples. We point out a key equality which may not hold for NA samples. Thus, the results of those empirical Bayes procedures based on NA samples are dubious     

基于扩散矩阵的半监督回归学习

半监督学习算法用到标记和未标记的样本.大量的实验表明,利用无标记样本可以改进学习算法的逼近性能.然而,当样本数增加时,逼近性能的定量分析几乎没有.本文构造基于扩散矩阵的一种半监督学习算法,建立逼近阶.结果还量化地说明,未标记样本的使用可以减少逼近误差.     

Robust numerical integration and pairwise independent random variables

A numerical integration method by means of random samples is called robust, if the variance of its error is as small as that of the i.i.d.-sampling for any integrand. To reduce the randomness of robust numerical integration, we use pairwise random samples instead of i.i.d. samples. Among others, we recommend the discrete random Weyl sampling for the quick generation of pairwise independent samples.     

Reconstruction of Bandlimited Functions from?Unsigned Samples

We consider the recovery of real-valued bandlimited functions from the absolute values of their samples, possibly spaced nonuniformly. We show that such a reconstruction is always possible if the function is sampled at more than twice its Nyquist rate, and may not necessarily be possible if the samples are taken at less than twice the Nyquist rate. In the case of uniform samples, we also describe an FFT-based algorithm to perform the reconstruction. We prove that it converges exponentially rapidly in the number of samples used and examine its numerical behavior on some test cases.     

Bayesian approach to prediction and the spacings in the exponential distribution

Summary A series of independent samples are drawn from a general population with positive variationf(x,ϕ), x>0. Based on the Bayesian approach, a general predictive distribution is given, to predict a statistic in the future sample based on the statistics in the earlier samples (or stages). Few general classes of distributions of this type like Koopman-Pitman family, power function family and Burr's class of distributions are considered to show how this procedure works in predicting order statistics in the future sample. Also, the sum of the spacings in the future samples from an exponential population is predicted in terms of similar sum of spacings in the earlier samples. Discussion on the variance of this predictive distribution is dealt with. Finally, an illustrative example with simulated samples from an exponential population gives actual prediction of an order statistic as well as the sum of spacings in the future sample.     

基于收缩近邻方法的征信缺失数据插补研究

在海量征信数据的背景下,为降低缺失数据插补的计算成本,提出收缩近邻插补方法.收缩近邻方法通过三阶段完成数据插补,第一阶段基于样本和变量的缺失比例计算入样概率,通过不等概抽样完成数据的收缩,第二阶段基于样本间距离,选取与缺失样本近邻的样本组成训练集,第三阶段建立随机森林模型进行迭代插补.利用Australian数据集和中国各银行数据集进行模拟研究,结果表明在确保一定插补精度的情况下,收缩近邻方法较大程度减少了计算量.