永久随机数(PRNs)在工业抽样调查中的应用

鉴于规模以下工业抽样调查目前没有考虑样本轮换问题,只是要求尽可能保持样本单位的相对稳定,但如何保持原有样本单位并增加新样本,以保持一个相对固定的样本规模,目前没有具体的方法。利用永久随机数不仅可以抽取分层抽样中每层的样本,而且还可在连续性调查中增加所需样本或进行样本轮换,具有广泛推广与应用的价值。     

永久随机数在多层次调查中的应用

采用永久随机数法抽样技术抽到的样本具有很好地样本兼容的性质。本文讨论了采用序贯srswor抽样技术和Poisson抽样技术这两种永久随机数法抽样技术时,多层次调查的实现问题,希望能为多层次调查的实现提供一点启示。     

关于永久随机数法抽样技术的探讨

永久随机数法(Permanent Random Numbers)抽样技术在各国调查实践中有着广泛的应用,主要集中在农业、能源、商业、价格指数调查等方面.近年来我国抽样调查领域也开始引入永久随机数法抽样技术,如规模以下工业抽样调查、农村抽样调查等等.本文系统介绍了永久随机数法抽样技术的几种常用的方法,并对其特点进行探讨,希望能够促进永久随机数法抽样技术在我国抽样调查体系中的应用和推广.     

浅谈永久随机数技术在抽样调查实践中的应用

永久随机数（Permanent Random NumbersPRNs）技术在各国的抽样调查实践中有着广泛的应用。近年来，我国抽样调查领域也开始引入永久随机数法抽样技术，如规模以下工业抽样调查等。本文根据本人近年来在规模以下工业抽样调查学习和实践中的一些体会，介绍永久随机数法抽样技术的几种应用，希望能够为其它专业的抽样调查实践提供一些参考和借鉴作用，以促进永久随机数法抽样技术在统计调查中的应用和推广。     

多变量与规模成比例概率抽样的有关问题

多变量与规模成比例概率抽样技术是永久随机数抽样技术与Poisson抽样技术的结合与发展。文章讨论了多变量与规模成比例概率抽样的基本原理,并对其实用价值进行述评,希望能促进该抽样技术在调查工作中的应用。     

样本轮换在劳动工资抽样调查中的应用探讨

针对目前我国劳动工资抽样调查中存在长期样本不轮换而导致劳动工资数据与实际不符的情况,在借鉴国内外样本轮换技术应用实践和经验的基础上,结合上海劳动工资季度调查的实际情况,初步构造了劳动工资抽样调查中的子样本轮换方法,并指出了劳动工资抽样调查进一步改进的方向.     

连续性抽样调查中的样本轮换研究

 内容摘要：本文首先介?绍了样本轮换研究问题提出的背景和国内外研究现状。接着介绍了分层抽样下样本轮换的理论模型。包括分层抽样下样本轮换的估计量公式和最优样本轮换率的确定方法。再接着利用前面介绍的理论知识,结合上海市城镇住房空置率抽样调查数据进行实证分析。由于该抽样调查采取的是分层抽样,因此相应地用分层抽样下的样本轮换研究。先根据该抽样调查本身的特点和社会经济活动的规律确定样本轮换时间间隔为1年。再分别计算出各层的最优样本轮换率和总体的样本轮换率。最后分别对三层子总体样本轮换的效果进行分析,分析发现各层经过样本轮换以后的精度比不进行样本轮换或进行完全样本轮换的精度有了明显的提高,轮换效果显著。     

中国劳动力调查的另一种四层次样本轮换方法

针对中国劳动力调查在部分省级单位内采用的四阶段抽样设计,构造了一种四级单元连续调查五次时的四层次样本轮换方法。该方法中,一级单元采用样本轮换模式40 in,二级单元采用样本轮换模式20 in,三级单元采用样本轮换模式10 in,四级单元采用样本轮换模式5 in。该方法保证了各级单元的样本量在轮换过程中不发生变化,同时还保证了四级单元在相邻两个季度和相邻两年的相同季度时均具有一定的拼配样本。     

二维平衡单水平轮换模式设计研究

现行的轮换样本调查使用各种类型的单水平轮换模式,在西方各国均得到了广泛应用,但是也存在着一系列问题。因此,通过对各种类型的轮换模式进行统一,并进行系统化、理论化研究,最终得出了二维平衡单水平轮换模式设计方法,并对其应用优势进行了总结。这套设计方法不仅将轮换模式设计与后续的估计方法研究统一起来,而且还能够削减各类轮换偏差的负面影响,并能准确度量轮换样本之间的相关关系,最终得出更加准确的连续性抽样估计量。     

轮换样本调查的轮换模式设计与估计方法

文章以我国城市住户调查的轮换模式设计为例,研究了轮换样本调查中的轮换模式设计与估计方法等问题.不完全单水平轮换模式是轮换样本调查中非常理想的一种轮换模式,既吸收了单水平轮换模式的优点,又充分体现了轮换样本调查的优势.文章所研究的这套轮换模式设计与估计方法不仅适合在我国城市住户抽样调查中使用,而且也可推广应用到我国政府统计部门开展的其他类型的连续性抽样调查中.     

Conditional and Restricted Pareto Sampling: Two New Methods for Unequal Probability Sampling

Abstract. Two new unequal probability sampling methods are introduced: conditional and restricted Pareto sampling. The advantage of conditional Pareto sampling compared with standard Pareto sampling, introduced by Rosén (J. Statist. Plann. Inference, 62, 1997, 135, 159), is that the factual inclusion probabilities better agree with the desired ones. Restricted Pareto sampling, preferably conditioned or adjusted, is able to handle cases where there are several restrictions on the sample and is an alternative to the recent cube method for balanced sampling introduced by Deville and Tillé (Biometrika, 91, 2004, 893). The new sampling designs have high entropy and the involved random numbers can be seen as permanent random numbers.     

Estimation of the means of the bivariate normal using moving extreme ranked set sampling with concomitant variable

The estimation of the means of the bivariate normal distribution, based on a sample obtained using a modification of the moving extreme ranked set sampling technique (MERSS) is considered. The modification involves using a concomitant random variable. Nonparametric-type methods as well as the maximum likelihood estimation are considered. The estimators obtained are compared to their counterparts based on simple random sampling (SRS). It appears that the suggested estimators are more efficient. Also, MERSS with concomitant variable is easier to use in practice than the usual ranked set sampling (RSS) with concomitant variable. The issue of robustness of the procedure is addressed. Real trees data set is used for illustration.     

On the use of secondary capture-recapture samples to estimate temporary emigration and breeding proportions

The use of the Cormack-Jolly-Seber model under a standard sampling scheme of one sample per time period, when the Jolly-Seber assumption that all emigration is permanent does not hold, leads to the confounding of temporary emigration probabilities with capture probabilities. This biases the estimates of capture probability when temporary emigration is a completely random process, and both capture and survival probabilities when there is a temporary trap response in temporary emigration, or it is Markovian. The use of secondary capture samples over a shorter interval within each period, during which the population is assumed to be closed (Pollock's robust design), provides a second source of information on capture probabilities. This solves the confounding problem, and thus temporary emigration probabilities can be estimated. This process can be accomplished in an ad hoc fashion for completely random temporary emigration and to some extent in the temporary trap response case, but modelling the complete sampling process provides more flexibility and permits direct estimation of variances. For the case of Markovian temporary emigration, a full likelihood is required.     

Record-breaking data: a parametric comparison of the inverse-sampling and the random-sampling schemes

In many industrial quality control experiments and destructive stress testing, the only available data are successive minima (or maxima)i.e., record-breaking data. There are two sampling schemes used to collect record-breaking data: random sampling and inverse sampling. For random sampling, the total sample size is predetermined and the number of records is a random variable while in inverse-sampling the number of records to be observed is predetermined; thus the sample size is a random variable. The purpose of this papper is to determinevia simulations, which of the two schemes, if any, is more efficient. Since the two schemes are equivalent asymptotically, the simulations were carried out for small to moderate sized record-breaking samples. Simulated biases and mean square errors of the maximum likelihood estimators of the parameters using the two sampling schemes were compared. In general, it was found that if the estimators were well behaved, then there was no significant difference between the mean square errors of the estimates for the two schemes. However, for certain distributions described by both a shape and a scale parameter, random sampling led to estimators that were inconsistent. On the other hand, the estimated obtained from inverse sampling were always consistent. Moreover, for moderated sized record-breaking samples, the total sample size that needs to be observed is smaller for inverse sampling than for random sampling.     

基于平衡轮换样本调查的季节调整方法研究

传统季节调整方法对时间序列数据进行季节调整时,往往假定误差项为白噪声,不考虑其序列相关关系。为了进行更准确地季节调整分析,本文从连续性抽样调查的角度出发,研究基于平衡轮换样本调查的抽样误差对季节调整的影响,建立一般化的季节调整模型,利用卡尔曼滤波进行参数估计,并从预测误差、误差方差等角度评价模型精度。最后以中国城镇住户调查采用的12～0平衡轮换模式为例,对考虑抽样误差结构特征的季节调整模型进行实证分析,验证这套季节调整方法的有效性。     

On the use of secondary capture-recapture samples to estimate temporary emigration and breeding proportions

The use of the Cormack-Jolly-Seber model under a standard sampling scheme of one sample per time period, when the Jolly-Seber assumption that all emigration is permanent does not hold, leads to the confounding of temporary emigration probabilities with capture probabilities. This biases the estimates of capture probability when temporary emigration is a completely random process, and both capture and survival probabilities when there is a temporary trap response in temporary emigration, or it is Markovian. The use of secondary capture samples over a shorter interval within each period, during which the population is assumed to be closed (Pollock's robust design), provides a second source of information on capture probabilities. This solves the confounding problem, and thus temporary emigration probabilities can be estimated. This process can be accomplished in an ad hoc fashion for completely random temporary emigration and to some extent in the temporary trap response case, but modelling the complete sampling process provides more flexibility and permits direct estimation of variances. For the case of Markovian temporary emigration, a full likelihood is required.     

Estimating probabilities from invariant permutation distributions

Summary We introduce variance reduction techniques as general tools for estimating probabilities from invariant permutation distributions. The paper discusses importance sampling, antithetic sampling and control variates sampling as alternatives to uniform Monte Carlo sampling for estimating exact critical values orP-values in a broad class of permutation tests. Results may be extended to permutation confidence intervals and linear rank tests. An asymptotic theory is provided for each proposed variance reduction method. Invited paper at the Conference held in Bologna, Italy, 27–28 May 1993, on ?Statistical Tests: Methodology and Econometric Applications?.     

A List Sequential Sampling Method Suitable for Real-Time Sampling

Abstract.  A flexible list sequential π ps sampling method is introduced and studied. It can reproduce any given sampling design without replacement, of fixed or random sample size. The method is a splitting method and uses successive updating of inclusion probabilities. The main advantage of the method is in real-time sampling situations where it can be used as a powerful alternative to Bernoulli and Poisson sampling and can give any desired second-order inclusion probabilities and thus considerably reduce the variability of the sample size.     

Randomized nomination sampling for finite populations

We propose a randomized minima–maxima nomination (RMMN) sampling design for use in finite populations. We derive the first- and second-order inclusion probabilities for both with and without replacement variations of the design. The inclusion probabilities for the without replacement variation are derived using a non-homogeneous Markov process. The design is simple to implement and results in simple and easy to calculate estimators and variances. It generalizes maxima nomination sampling for use in finite populations and includes some other sampling designs as special cases. We provide some optimality results and show that, in the context of finite population sampling, maxima nomination sampling is not generally the optimum design to follow. We also show, through numerical examples and a case study, that the proposed design can result in significant improvements in efficiency compared to simple random sampling without replacement designs for a wide choice of population types. Finally, we describe a bootstrap method for choosing values of the design parameters.     

Estimation of population mean and variance in flock management: a ranked set sampling approach in a finite population setting

Ranked set sampling is a sampling technique that provides substantial cost efficiency in experiments where a quick, inexpensive ranking procedure is available to rank the units prior to formal, expensive and precise measurements. Although the theoretical properties and relative efficiencies of this approach with respect to simple random sampling have been extensively studied in the literature for the infinite population setting, the use of ranked set sampling methods has not yet been explored widely for finite populations. The purpose of this study is to use sheep population data from the Research Farm at Ataturk University, Erzurum, Turkey, to demonstrate the practical benefits of ranked set sampling procedures relative to the more commonly used simple random sampling estimation of the population mean and variance in a finite population. It is shown that the ranked set sample mean remains unbiased for the population mean as is the case for the infinite population, but the variance estimators are unbiased only with use of the finite population correction factor. Both mean and variance estimators provide substantial improvement over their simple random sample counterparts.