基于非平衡面板数据的总体比例变动的检验方法

文章针对添加新生单元固定样本纵向调查设计和非平衡面板数据,研究不等比例抽样情形下,两个连续时点间总体比例变动的显著性检验方法.基于两种处理重叠样本的思路和对随机抽样情形下相关研究方法的改进,提出了两种新方法,用来估计不等比例抽样情形下总体比例之差的方差,进而检验总体比例变动的显著性.     

层次Bayes模型在抽样调查中的应用

在抽样调查中,经常需要估计总体中具有某一特征的个体单位的比例p,当我们所关心的这个比例很小,或者是样本量n很小时,通过样本得到的估计就不是很好。文章构造了层次Bayes模型,利用参数和超参数的先验分布推导出总体的后验分布,通过随机模拟了解比例p的大致分布情况。这种随机模拟的方法计算速度很快,而且准确性也较高。     

网络访问固定样本调查的统计推断研究

如何解决网络访问固定样本调查的统计推断问题,是大数据背景下网络调查面临的严重挑战。针对此问题,提出将网络访问固定样本的调查样本与概率样本结合,利用倾向得分逆加权和加权组调整构造伪权数来估计目标总体,进一步采用基于有放回概率抽样的Vwr方法、基于广义回归估计的Vgreg方法与Jackknife方法来估计方差,并比较不同方法估计的效果。研究表明:无论概率样本的样本量较大还是较小,本研究所提出的总体均值估计方法效果较好,并且在方差估计中Jackknife方法的估计效果最好。     

关于样本对总体代表性问题的认识与讨论——兼论抽样调查中辅助变量的作用

一、问题的提出抽样调查作为非全面调查最重要、应用最为广泛的一种方法 ,是按一定程序 (方法 )从总体中抽取一部分单元 ,即样本进行调查 ,根据样本数据对总体目标量作估计或其他推断。根据样本估计总体 ,也即根据部分来估计整体必然存在误差 ,即抽样误差。人们期望这种误差愈小愈好 ,估计愈精确愈好。用什么样的抽样方法和估计方法能使这种抽样估计更为正确 ?用一种目前流行的说法就是 ,什么样的样本对总体有较好的代表性 ?目前在国内 ,不少关心或从事抽样调查的统计工作者 ,对上述所谓“样本对总体的代表性”问题 ,不同程度地存在一些认识…     

提高抽样调查样本代表性的措施

抽样调查的目的是通过有限的样本的指标(样本平均数,样本成数)来估计总体(population)的参数(平均数,总体成数),由于用样本来估计总体,所以人们期望样本能如实地反映总体、代表总体,因而如何提高样本的代表性,就成为当前研究的热点之一。一、样本代表性的含义目前在国内,不少关心或从事抽样调     

决策规则下符合性阶段审计抽样时取样规模的确定

符合性检查是抽样审计的重要组成内容之一,它主要用于判断审计对象总体错误发生比率,被审计单位内部控制制度的执行及其改进情况等。符合性比例估计是符合性抽样推断的主要内容,文章根据决策规则的基本原理,对阶段审计抽样下的符合性比例估计所需要的样本数目进行了讨论。     

总体不稳定情形下的抽样

要估计报告期的时期指标，样本当然要在期初抽出。可是在报告期内，可能有的单位离开了总体，有的些单位中途进入总体。于是，期初样本便不能代表变化了的总体。怎样处理这种情况？在这种情形下怎样作子总体的估计？本文提出了三种方法     

基于平衡样本的最优模型辅助抽样策略

模型辅助方法的思想是基于抽样设计借助于超总体模型获得对总体参数的有效推断.满足辅助变量的HT估计等于总体总量真值的样本被称为平衡样本.对于平衡样本,如果超总体模型的异方差性可以通过辅助变量解释,由此得出最优抽样策略:平衡抽样设计与HT估计结合是最优策略,包含概率正比于模型残差的标准差.     

大数据背景下网络调查样本的随机森林倾向得分模型推断研究

随着大数据与互联网技术的迅猛发展,网络调查的应用越来越广泛。本文提出网络调查样本的随机森林倾向得分模型推断方法,通过构建若干棵分类决策树组成随机森林,对网络调查样本单元的倾向得分进行估计,从而实现对总体的推断。模拟分析和实证研究结果表明:基于随机森林倾向得分模型的总体均值估计的相对偏差、方差与均方误差均比基于Logistic倾向得分模型的总体均值估计的相对偏差、方差与均方误差小,提出的方法估计效果更好。     

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...     

Sampling efficiency for an alternating poisson process

The efficiency of schemes for sampling an alternating Poisson process (0,1 observations) is evaluated by the inverse ratio of the variance of the proportion estimate, p, to the binomial variance. The variance ratio presented by D.R. Cox (in Renewal Theory) for fixed interval sampling is generalized to accommodate random sampling and random sampling after a time delay equal to a fixed proportion, γ , of the mean time between observations, δ. The result is a sampling design tool that provides quantifications for the effect of various spacings between observations and of fixed vs. random sampling. Direct application is made to thes field of work sampling.     

Estimation of parameters of the inverse gaussian-weibull mixture model

The main objective of this work is to estimate the 5-dime-nsional vector of parameters (p,μ,λ,α,c) of the mixture of an Inverse Gaussian IG(μ,λ) and Weibull W(α,c) distributions with mixing proportion p. We use the maximum Likelihood method (MLM) and the weighted maximum likelihood method (WMLM), both under the sampling schemes suggested by Hosmer (1973). Simulation study shows that the WMLM performs best, when Hosmer's model 2 is used, in the sense of minimizing the mean square error.     

Monitoring the conforming fraction of high-quality processes using a control chart p under a small sample size and an alternative estimator

In this paper, we propose to change the traditional monitored statistic in a control chart p, by changing the sampling proportion ${\hat{p}}$ to a new statistics denoted as ${\tilde{p}}$ . We aim to minimize problems in designing the control chart p for high quality processes when only a small sample size is available. The idea of the new statistics is simple, as it involves taking two independent samples of a Bernoulli population. From each sample, the sampling proportion is calculated, and the new statistic to monitor is the weighted mean of the sampling proportion of each sample employed to weight the overall sampling proportion. We note that the control chart p that employs the new ${\tilde{p}}$ statistic provides more in-control values of average run length closer to the usual fixed value of 370 than the traditional statistic, that is, the sampling proportion. Numerical examples illustrate the new proposal.     

Lystematic sampling of two-dimensional surfaces and related problems

The paper is largely concerned with twenty-one possible methods of sampling a plane area, with points as sampling units, for the purpose of estimating a portion of this area having certain defined characteristics. These methods result from a combination, two at a time, of random, stratified and systematic sampling in two perpendicular directions, with or without alignment of the sampled points. Eleven of these methods involve systematic sampling in one or both directions. The estimate of the proportion of the area of interest is simply the proportion of points in the total area, falling within the area having the defined characteristics. For each method the variance function is derived. Fourteen different types of space covariance functions are involved in these variance functions.     

Estimating the population median by nomination sampling

In nomination sampling, the largest values from several independent random samples (nominees) are rank ordered, and an estimate of the population median is formed by interpolating between 2 of these order statistics. The resulting estimate compares favorably to the sample median of a simple random sample from the same population. When historical data sets retain only extreme values, nomination sampling may offer the only practical way to estimate the population median. The approach may also be useful when potential survey respondents will only participate if they can actively influence the selection of cases for analysis.     

Estimating bird species richness from capture and count data

We used capture-recapture methods to estimate bird species richness from mist-net and point-count data from a study area in Campeche, Mexico. We estimated species richness separately for each survey technique for two habitats, forest and pasture, in six sampling periods. We then estimated richness based on species' detections by either technique, and estimated the proportion of species detected by each technique that are not part of the population sampled by the other technique. No consistent differences existed between richness estimates from count data and from capture data in the two habitats. In some sampling periods, over 50% of the richness estimate from one survey technique may be species that are not sampled by the other technique, suggesting that one technique may not be adequate to estimate total species richness and that comparing estimates from areas sampled by different techniques may not be valid.     

Estimation of a sensitive proportion by Warner’s randomized response data through inverse sampling

It is difficult to obtain trustworthy data on stigmatizing matters like habits of tax evasion, drunken driving, etc., through direct queries. To overcome this difficulty, Warner introduced randomized response techniques to estimate the proportion of people bearing such a stigmatizing or sensitive characteristic in a given community. For simplicity in estimation he restricted to SRSWR and since then, several researchers have extended and applied this technique in various ways. All these results involve sampling with a pre-fixed number of draws. In this paper we employ inverse sampling with equal probabilities with replacement and show certain advantages in estimation using randomized response data by Warner’s device gathered through such a simple inverse sampling scheme.     

Estimating sensitive proportions by Warner’s randomized response technique using multiple randomized responses from distinct persons sampled

In 1965 Warner pioneered randomized response techniques to estimate the proportion of people bearing a sensitive characteristic. He restricted applying his randomized response device to gather data on sensitive issues from respondents chosen by simple random sampling with replacement (SRSWR). It has spawned numerous ramifications. We present results for the situation where the distinct persons chosen in an SRSWR are identified but each one independently gives a randomized response by Warner’s device, repeated as many times as he/she is selected. Two new estimators are proposed for the sensitive proportion and compared against relevant competitors.     

Effective Rotation Patterns Under Non Response in Two-Occasion Successive Sampling

This article intends to develop some effective rotation patterns with the aid of attractive imputation methods when the problems of non response occur in two-occasion successive sampling. Utilizing the information on p (p ??1) auxiliary variables regression methods of imputation have been considered and subsequently multiple linear regression type estimators are proposed to estimate the current population mean in two-occasion successive sampling. Proposed estimators are compared with the estimator for same situations but in the absence of non-response. Optimum replacement strategies of the respective estimators have been discussed and results are interpreted with the help of empirical studies. Conclusions and suitable recommendations are made.     

Designing sampling plans to capture rare objects

This article focuses on two‐phase sampling designs for a population with unknown number of rare objects. The first phase is used to estimate the number of rare or potentially rare objects in a population, and the second phase to design sampling plans to capture a certain number or a certain proportion of such type of objects. A hypergeometric‐binomial model is applied to infer the number of rare or potentially rare objects and Monte Carlo simulation based approaches are developed to calculate needed sample sizes. Simulations and real data applications are discussed. The Canadian Journal of Statistics 37: 417–434; 2009 © 2009 Statistical Society of Canada