论多主题分层抽样

一、分层方法在多主题分层抽样中,层的确定对多主题分层抽样效果的好坏起着决定作用。在多主题分层抽样中,抽样框有两种,即无序抽样框和有序抽样框。在这两种抽样框中,对多主题总体进行分层的方法是不同的。(一)无序抽样框的分层无序抽样框是指将多主题总体不按任何标志排队,而只是将多主题总体中的各个总体单位随机排列而形成的抽样框。我们采用聚类分析的方法来对多主题总体进行分层。     

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.     

多主题分层抽样的分层技术

多主题分层抽样的分层技术肖玲多主题分层抽样中层的确定是一个十分关键的问题,分层是否合理决定着多主题分层抽样的抽样效果的优劣。而判断分层是否合理,分层效果如何,应该从两个方面考虑：（１）合理的分层数及各层内所包含的总体单位。（２）样本单位在各层中的合理...     

分层抽样条件下Logistic回归模型的参数估计方法

一、引言Ｌｏｇｉｓｔｉｃ回归模型是对二分类因变量 (因变量ｙ只取两个值 )进行回归分析时经常使用的统计分析方法。与线性回归不同 ,Ｌｏｇｉｓｔｉｃ回归是一种非线性模型 ,因而普遍采用的参数估计方法是最大似然估计法。可以证明 ,在随机样本条件下 ,Ｌｏｇｉｓｔｉｃ模型的最大似然估计具有一致性、渐进有效性和渐进正态性。然而在有些问题的研究中 ,样本抽取并不完全是随机的 ,而是采用分层抽样方法 ,首先将研究总体按属性特征分类 ,然后在各类中随机抽取样本 ,这就需要考虑分层抽样条件下Ｌｏｇｉｓｔｉｃ模型的参数估计问题。对分层…     

极小信息量下分层抽样的样本分配党的一个案例

一、问题的提出分层抽样中样本量在各层中如何分配,这是抽样设计中的一个重要问题。计算各层的样本量需要一些辅助信息,如各层中目标变量的方差。在抽样调查的实践中,特别是一次性的抽样调查中,上述所需的辅助信息常常不具备,因此,我们面临着在信息量最小的条件下如何在各层中分配样本量的问题。本文产生于作者在美国ＮＯＲＣ(ＮａｔｉｏｎａｌＯｐｉｎｉｏｎＲｅｓｅａｒｃｈＣｅｎｔｅｒ)进行研究期间所做的调查设计中的一个实例,这里对其进行了归纳,加工,提炼与分析,希望能够就极小信息量条件下如何在分层抽样中进行样本量的分配这一问题…     

关于分层抽样的一点思考

一、问题的提出在2002年剑阁县103个私营企业和30个城镇集体以上商业企业的劳资抽样中,由于离差太大,根据极限误差(相对误差与块内平均数的乘积)及块内方差(“块”字是针对剑阁县对全社会劳资抽样调查时取的,与“层”实质上是一个含义)等计算的必要抽样单位数与全部单位数相差无几。运用抽样比例不等的“奈曼配置”方法来再次分层抽取样本还是不行,经反复思考研究,我们摸索出了一种新方法,至于这种新方法的取名,暂确定为“多次中点分层抽样”。二、多次中点分层抽样的基本原理以累计人数不断按中点分层(层间组距不等,基本上呈几何递减趋势);…     

对资金流量表的改进

对资金流量表的改进孙静娟ＡＢＳＴＲＡＣＴＢａｓｅｄｏｎｔｈｅｓｙｓｔｅｍａｔｉｃｄｉｓｃｕｓｓｉｏｎｏｎｔｈｅｃｏｍｐｌｉｃａｔｅｄａｎｄｏｒｄｅｒｅｄｑｕａｎｔａｔｉｖｅｒｅｌａｔｉｏｎｓｉｎｔｈｅｉｎｃｏｍｅｄｉｓｔｒｉｂｕｔｉｏｎａｎｄｕｔｉｌ...     

论复合分层及按农户规模分层抽样法——兼论抽样权偏误的矫正

在我国近几年农村抽样调查中,一般是应用“按有关标志排队,等距抽样”的方法。随着形势的发展,这种方法的局限性不断显露出来了。农村经济调查,主要是农产量与经济收入调查,就农民经济收入调查来说,“有关标志”实际上往往就是调查的标志本身,即按往年农户收入高低排队,然后等距抽取农户。这在理论上是较为理想的方法,但在实际上很难     

对改进中国失业统计方法的探讨

失业问题是人类进入工业化以来一直困扰世界各国的经济和社会问题 ,也是与债务、通货膨胀并列的世界三大难题之一。中国在步入市场经济的过程中 ,同样不可避免地遇到了失业问题的困扰。目前在中国失业现象已普遍存在 ,并且成为影响社会稳定与经济发展的一个非常不利的因素 ,因而引起了社会各界的高度重视。但长期以来 ,中国一直否认社会主义社会有失业问题存在 ,导致国家失业统计纪录的真空。直到 1 993年有关统计部门才将原来所谓的“待业人员”改称为“失业人员” ,这虽然是一个历史性的进步 ,但在失业统计的理论方法以及具体操作环节上却…     

对宏观经济监测预警分析系统的改进

 我国于80年代中期开始了对宏观经济监测预警系统的研究,90年代初一些单位和部门相继开展了宏观经济的景气调查和监测预警的分析工作,目前从事宏观经济景气调查和分析的主要单位和部门有：由国家计委综合司、国家统计局统计科学研究所和国民经济综合统计司采用经济景气综合指数和灯号系统进行景气预测分析、判断经济景气状态和波动趋势;中国人民银行调查统计司每月、国家信息中心预测部按季分别对全国5000家企业单位进行景气调查。1994年6月24日,前4家单位共同发起和组织了经济景气分析预警联席会议,旨在通过加强相互间的合作和交流,进一步提高我国的景气分析预警工作的水平。到目前为止,这几家单位已经按季召开了9次经济景气分析预警联席会议,这对于各部门相互间的合作和交流,加强和改善我国的宏观调控水平起到了积极的作用。但是我们在开展这项工作中还存在许多不足,急需改进。     

Calibration estimator of population mean under stratified ranked set sampling design

Calibration method adjusts the original design weights to improve the estimates by using auxiliary information. In this article we have proposed new calibration estimators under stratified ranked set sampling design and derive the estimator of variance of calibration estimator. A simulation study is carried out to see the performance of proposed estimators.     

Optimum multivariate stratified double sampling design: Chebyshev's Goal Programming approach

In stratified sampling when strata weights are unknown a double sampling technique may be used to estimate them. A large simple random sample from the unstratified population is drawn and units falling in each stratum are recorded. A stratified random sample is then selected and simple random subsamples are obtained out of the previously selected units of the strata. This procedure is called double sampling for stratification. If the problem of non-response is there, then subsamples are divided into classes of respondents and non-respondents. A second subsample is then obtained out of the non-respondents and an attempt is made to obtain the information by increasing efforts, persuasion and call backs. In this paper, the problem of obtaining a compromise allocation in multivariate stratified random sampling is discussed when strata weights are unknown and non-response is present. The problem turns out to be a multiobjective non-linear integer programming problem. An approximation of the problem to an integer linear programming problem by linearizing the non-linear objective functions at their individual optima is worked out. Chebyshev's goal programming technique is then used to solve the approximated problem. A numerical example is also presented to exhibit the practical application of the developed procedure.     

Calibration approach estimation of the mean in stratified sampling and stratified double sampling

Calibration estimation improves the precision of the estimates of population parameters by incorporating specified auxiliary information. A class of calibration estimators has been proposed for estimating the population mean by making use of a set of calibration constraints in stratified sampling. The estimator of variance of the proposed calibration estimator of the mean is derived using a lower level calibration approach. The idea is extended for stratified double sampling. A simulation study is used to evaluate the performances of the proposed estimators by comparing them with the similar estimators developed by Tracy, Singh and Arnab (2003 Tracy, D.S., Singh, S., Arnab, R. (2003). Note on calibration in stratified and double sampling. Surv. Methodol. 29(1): 99–104. [Google Scholar]) based on different sets of calibration constraints.     

A control in stratified sampling

A new expression of the variance of the units from a stratified finite population wit L strata, is obtained in funcions of the means L–1 strata, and in conseqeuence we obtained formulae which relates the mean of a stratum in a statistical study with the population mean and the ones of the remaining strate. As an application, we obtained a useful checking of the estimation consistency by stratified sampling in any precies survey     

Randomized response in stratified sampling

This paper extends the randomized response model presented in Christofides (Metrika 57 (2003)195) to the case of stratified sampling. A comparison between the application of the technique in stratified and simple random sampling is presented. In addition, the model is compared with the randomization technique in stratified sampling of Kim and Warde (J. Statist. Plann. Inference 120 (2004) 155).     

New calibration estimator in stratified sampling

This paper develops the theory of calibration estimation and proposes calibration approach alternative to existing calibration estimators for estimating population mean of the study variable using auxiliary variable in stratified sampling. The theory of new calibration estimation is given and optimum weights are derived. A simulation study is carried out to performance of the proposed calibration estimator with other existing calibration estimators. The results reveal that the proposed calibration estimators are more efficient than Tracy et al., Singh et al., Singh calibration estimators of the population mean.     

An adaptive allocation sampling design for which the conventional stratified estimator is an appropriate estimator

Adaptive allocations in stratified sampling design are suitable for studying Biological and Environmental populations. Biologists tend to use the conventional stratified estimator for an adaptive allocation sampling design in spite of its adaptive nature. In this paper, we have introduced an adaptive allocation sampling design for which the conventional stratified estimator has some desired statistical properties. We conduct a simulation study on a real population. The results show that the conventional stratified estimator for the introduced adaptive allocation sampling design is more efficient than other available estimators.     

Efficient estimators for adaptive stratified sequential sampling

In stratified sampling, methods for the allocation of effort among strata usually rely on some measure of within-stratum variance. If we do not have enough information about these variances, adaptive allocation can be used. In adaptive allocation designs, surveys are conducted in two phases. Information from the first phase is used to allocate the remaining units among the strata in the second phase. Brown et al. [Adaptive two-stage sequential sampling, Popul. Ecol. 50 (2008), pp. 239–245] introduced an adaptive allocation sampling design – where the final sample size was random – and an unbiased estimator. Here, we derive an unbiased variance estimator for the design, and consider a related design where the final sample size is fixed. Having a fixed final sample size can make survey-planning easier. We introduce a biased Horvitz–Thompson type estimator and a biased sample mean type estimator for the sampling designs. We conduct two simulation studies on honey producers in Kurdistan and synthetic zirconium distribution in a region on the moon. Results show that the introduced estimators are more efficient than the available estimators for both variable and fixed sample size designs, and the conventional unbiased estimator of stratified simple random sampling design. In order to evaluate efficiencies of the introduced designs and their estimator furthermore, we first review some well-known adaptive allocation designs and compare their estimator with the introduced estimators. Simulation results show that the introduced estimators are more efficient than available estimators of these well-known adaptive allocation designs.     

An improved estimation in stratified random sampling

ABSTRACT

The article suggests a class of estimators of population mean in stratified random sampling using auxiliary information with its properties. In addition, various known estimators/classes of estimators are identified as members of the suggested class. It has been shown that the suggested class of estimators under optimum condition performs better than the usual unbiased, usual combined ratio, usual combined regression, Kadilar and Cingi (2005 Kadilar, C., Cingi, H. (2005). A new ratio estimator in stratified sampling. Commun. Stat. Theory Methods 34:597–602.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]), Singh and Vishwakarma (2006 Singh, H.P., Vishwakarma, G.K. (2006). Combined ratio-product estimator of finite population mean in stratified sampling. Metodologia de Encuestas Monografico: Incidencias en el trabjo de Campo 7(1):32–40. [Google Scholar]) estimators and the members belonging to the classes of estimators envisaged by Kadilar and Cingi (2003 Kadilar, C., Cingi, H. (2003). Ratio estimator in stratified sampling. Biomet. J. 45:218–225.[Crossref], [Web of Science ®] , [Google Scholar]), Singh, Tailor et al. (2008 Singh, H.P., Agnihotri, N. (2008). A general procedure of estimating population mean using auxiliary information in sample surveys. Stat. Trans. 9(1):71–87. [Google Scholar]), Singh et al. (2009 Singh, R., Kumar, M., Chaudhary, M.K., Kadilar, C. (2009). Improved exponential estimator in stratified random sampling. Pak. J. Stat. Oper. Res. 5(2):67–82.[Crossref] , [Google Scholar]), Singh and Vishwakarma (2010 Singh, H.P., Vishwakarma, G.K. (2010). A general procedure for estimating the population mean in stratified sampling using auxiliary information. METRON 67(1):47–65.[Crossref] , [Google Scholar]) and Koyuncu and Kadilar (2010) Koyuncu, N., Kadilar, C. (2010). On improvement in estimating population mean in stratified random sampling. J. Appl. Stat. 37(6):999–1013.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar].     

Optimum allocation in multivariate stratified sampling design in the presence of nonresponse with Gamma cost function

In multivariate stratified sample survey with L strata, let p-characteristics are defined on each unit of the population. To estimate the unknown p-population means of each characteristic, a random sample is taken out from the population. In multivariate stratified sample survey, the optimum allocation of any characteristic may not be optimum for others. Thus the problem arises to find out an allocation which may be optimum for all characteristics in some sense. Therefore a compromise criterion is needed to workout such allocation. In this paper, the procedure of estimation of p-population means is discussed in the presence of nonresponse when the use of linear cost function is not advisable. A solution procedure is suggested by using lexicographic goal programming problem. The numerical illustrations are given for its practical utility.