论有关标志等距抽样总体指标的估计

论有关标志等距抽样总体指标的估计雷钦礼有关标志排队等距抽样是在我国发展起来的一种行之有效的独特抽样方法。多年来,我国统计学界一直致力于抽取方法的完善和改进,从“随机起点,顺序等距抽样”和“半距起点,顺序等距抽样”到“随机起点,对称等距抽样”,使抽取方...     

分层抽样估计方法新探

The paper puts forward two ways of thinking on stratified sampling estimation, the longitudinal auto-egression and multi-compound estimation method. The longitudinal auto-regression method probes into estimate of population mean based on the linear regression relationship between the means of the strata under investigation and ancillary characteristics. The multi-compound estimation method estimates the means of the strata separately by different methods and the population parameter is taken from these estimates. The paper further analyses conditions for the application of these methods; estimates and their error term; advantages and disadvantages and other problems of the two problems.     

基尼系数的抽样估计

The paper discusses the properties of statistics and precision of estimation when the sample statistics are used to estimate Gini Coefficient.     

多阶段抽样比率估计

目前 ,单阶段、两阶段的抽样理论较丰富 (见文献 [1])。三阶段以上的抽样理论不完善。在这篇文章中 ,我们讨论多阶段抽样比率估计 ,给出了样本估计抽样均方误差及其无偏估计的公式。首先将总体Ｍ个单位分为Ｒ个一级组 ,第ｉ1(1≤ｉ1≤Ｒ)个一级组包含Ｒｉ1个二级组 ,第ｉ1一级组中第ｉ2 (1≤ｉ2 ≤Ｒｉ1)个二级组包含Ｒｉ1,ｉ2 个三级组 ,… ,直到前ｎ - 2级组分别是第ｉ1,ｉ2 ,… ,ｉｎ -2 个的ｎ- 2级组包含Ｒｉ1,… ,ｉｎ-2 个ｎ - 1级组。第ｉｎ -1个 (1≤ｉｎ -1≤Ｒｉ1,… ,ｉｎ -2 )ｎ - 1级组包含Ｒｉ1,… ,ｉｎ -1个总体单位。则Ｍ…     

成数抽样的比率估计

成数抽样的比率估计李金昌ＡＢＳＴＲＡＣＴＴｈｅｐａｐｅｒｄｉｓｃｕｓｓｅｓｔｈｅｉｓｕｅｏｆｒａｔｉｏｅｓｔｉｍａｔｉｏｎｉｎｓａｍｐｌｅｓｕｒｖｅｙｏｆｐｒｏｐｏｒ－ｔｉｏｎａｌｎｕｍｂｅｒｓ．Ｔｈｅｅｓｔｉｍｅｔｏｒａｎｄｖａｒｉａｎｃｅｏｆｓａ...     

多主题抽样中两种估计方法的比较

一、简单估计 所谓简单估计就是以样本单位标志值的平均值来直接作为总体标志值的估计值。由于简单估计的方法比较简单,因而在我国目前抽样调查的实践中应用比较普遍。 在多主题系统抽样中,假设有m个主题X_1、X_2、X_3…X_m,多主题总体单位数为N,从这N个总体单位中,按系统抽样的方式抽取n个样本单位,则多主题总体标志值X的简单估计量X为:     

同总体二比例差的抽样估计

同总体二比例差的抽样估计赵俊康一、问题的提出总体比例（即总体中具有某一村征的单位数占全部总体单位数的比重）是抽样调查中的重要目标量之一。在比例估计中,根据研究目的,除了需要估计具有不同特征的单位数的比例之外,有时候还需要对这些比例间的差作出估计,尤其...     

论区间估计中抽样平均误差的随机变异

一、问题的提出 当我们从一个单位为N的总体中,随机抽取容量为n的样本时,这种同容量的样本可有C_N~n(确切地说是在不重复抽样时)。显然,每个样本的平均数x和标准差S都是独立随机变量。 这里借用高等院校文科试用教材《数理统计学》(天津人民出版社出版)中的一个例子,有100头猪在20天内的增重数据,它们近似于正态分布,这个总体的平均数为X=30磅,标准差σ=10磅。纯随机抽取容量n=10的样本十个。十个样本的有关统计量数据如下表:①     

Bootstrap方法在复杂抽样中的应用

一、Bootstrap方法简介Bootstrap方法是美国统计学家Bradley·Efron在1979年提出的一种处理非参数统计推断问题的方法。它的一般提法是:已知来自总体(Y,(?),F)的简单随机样本Y_n=(y_1,y_2,…,Y_n),其中F是一未知的分布函数。设R(Y_n,F)是我们感兴趣的样本函数,我们欲得到R(Y_n,f)的某些信息,如:R(Y_n,F)的分布函数、E_FR、Var_FR或P_F(R<2)等等;下标F表示在分布函数F下求期望、方差或概率。所谓Bootstrap方法,就是用样本Y_n构造出F的极大似然估计(?)_n(一般就用样本Y_n的经验分布函数F_n来近似);然后,从F_n中抽出大小为n的简单随机样本Y_n~*=(Y_1~*,Y_2~*,     

抽样方法中调查误差的控制

本文探讨如何对抽样调查中的调查误差加以硬性控制,限调查误差于最小范围内。如何提高调查人员的素质以减少调查误差不是本文的主题。调查误差与抽样误差有着辩证关系。通过抽样调查得到的样本的综合指标与总体相应的综合指标必定存在误差。其一是抽样误差,其二是调查误差。当调查误差一定时,抽样误差的大小决定全部误差的大小;当抽样误差一定时,调查误差的大小决定全部误差的大小。如要求全部误差控制在一定范围之内,实践证明,抽样误差与调查误差便存在着一个此长彼消的辩证关系。即:当样本量加大时,抽样误差减少,但因工作难度加大,人员增加,调查误差的控制难度亦就越大;样本量减少,抽样误差扩大,但调查误差易控制,可提高调查精度。两种情况使总体误差基本稳定在一个范围内。     

The Effect of a Covariate on Standard Error and Confidence Interval Width

The standard error of covariate-adjusted mean difference is not always smaller than that of the unadjusted mean difference despite the fact that adding a covariate is commonly believed to reduce the unexplained error variance. The covariate mean difference between the contrasted treatment conditions can inflate the standard error of the adjusted mean difference. If the covariate is viewed as randomly varying from one study to another, a minimum sample size can be found to attain a desired probability of reducing the standard error and the confidence interval width for the adjusted mean difference.     

Sample Sizes in the Interval Estimation of the Correlation Coefficient

An expression is derived for the maximum length of the interval estimator of the correlation coefficient, p, under bivariate normal assumptions. The prespecification of this minimum attainable precision and the confidence level results in an expression for the sample size required. An approximate expression for the sample size is proposed and is numerically shown to be as good as or better than that based on the Fisher's Z transformation.     

Sample Size Requirements for the Back-of-the-Envelope Binomial Confidence Interval

The simplest approximate confidence interval for the binomial parameter p, based on x successes in n trials, is

where c is a suitable percentile of the normal distribution. Because I ₀ is so useful in introductory teaching and for back-of-the-envelope calculation, it is desirable to have guidelines for deciding when it provides a good answer. (It is clearly unwise to use I ₀ when x is too near 0 or n.) This article proposes such guidelines, based on the criterion that I ₀ should differ from the exact Clopper-Pearson confidence interval by an amount that is small compared to the length of the interval.     

对小康标准测算方法的探讨

对小康标准测算方法的探讨宋克辉使人民生活达到小康水平,是我国到本世纪末要实现的重要战略目标。为使各级领导和广大干部、群众对全国和全地区的小康目标和小康目标的实现程度有比较具体的数量概念,国家统计局统计科学研究所、城市社会经济调查总队、农村社会经济调查...     

Generalised Rank Regression Estimator with Standard Error Adjusted Lasso

One of the standard variable selection procedures in multiple linear regression is to use a penalisation technique in least‐squares (LS) analysis. In this setting, many different types of penalties have been introduced to achieve variable selection. It is well known that LS analysis is sensitive to outliers, and consequently outliers can present serious problems for the classical variable selection procedures. Since rank‐based procedures have desirable robustness properties compared to LS procedures, we propose a rank‐based adaptive lasso‐type penalised regression estimator and a corresponding variable selection procedure for linear regression models. The proposed estimator and variable selection procedure are robust against outliers in both response and predictor space. Furthermore, since rank regression can yield unstable estimators in the presence of multicollinearity, in order to provide inference that is robust against multicollinearity, we adjust the penalty term in the adaptive lasso function by incorporating the standard errors of the rank estimator. The theoretical properties of the proposed procedures are established and their performances are investigated by means of simulations. Finally, the estimator and variable selection procedure are applied to the Plasma Beta‐Carotene Level data set.     

总体比例的区间估计和样本量的确定

通过平移计分置信区间中心点,修正和改善其覆盖概率的统计性能,获取抽样总体比例估计的置信区间。对于放回抽样以及总体容量N较大的不放回抽样,平移系数建议都取0.02 z4;对于较小N∈[50,1000]的不放回抽样,平移系数建议取0.024 z4。基于置信区间,给出了相应的明显优于传统的样本量公式。