PPS适应性整群抽样设计及估计

在传统适应性整群抽样的基础上,提出PPS适应性整群抽样,即采用PPS方法获取初次样本单元,然后对初次样本单元进行样本外推得到聚集网和最终样本。首先给出PPS适应性整群抽样设计,然后对该抽样机制下总体单元入样概率进行推导,构造得到了修正的HT统计量,并通过模拟研究揭示了估计量的良好性质。     

规模以下工业抽样设计研究

为推动规模以下工业抽样调查工作以及解决当前调查面临的有关问题,本文对抽样设计进行了改进研究。首先,本文对规模以下工业抽样设计演变过程进行系统梳理,总结了现行抽样设计充分利用双重抽样框设计和综合运用三种抽样方法的特点。其次,针对园区层企业密度高的特点,探索结合园区因素改进地域抽样设计,对园区层和非园区层分别抽样,解决调查中面临的非抽样误差问题,并调整辅助变量使其与核心指标相关性均较高,确保抽样推断精度,有效提高抽样调查效率。并以我国东 部某省为例进行实证模拟得到结合园区因素抽样设计对调查工作改进的结论。再次,针对我国各级政府管理需要以及局队业务分工优化调整情况,介绍了规模以下工业样本追加理论和实证应用的主要研究成果。最后,在大数据时代数据来源广泛的背景下,本文在多重抽样框设计以及利用辅助变量提升样本轮换推断精度方面提出了进一步改进抽样设计的思路。     

调查内比估计效果的数据模拟分析——关于简单抽样设计与PPS系统抽样设计的比较

一、引言抽样设计的效率在于充分利用已知的辅助信息。我们知道,当辅助信息与目标变量之间具有较高的相关时,采用比估计方法可以提高抽样效率;当抽样单位的大小与目标变量之间有相关时,采用PPS抽样方法可以提高抽样效率。那么可以设想,在有辅助信息可以利用时,同时采用PPS抽样和比估计就可以更加提高抽样效率。我们利用一个省的农业普查数据进行模拟分析,证实了这一点。比估计是相对于简单估计而言的。简单估计量只涉及所估计的指标本身,不需要利用其他信息。而在实际调查中,调查通常是多指标的,一个指标的估计常常可以利用其他指标或历…     

迭代方法在多目标总体抽样调查中的应用

一、问题的提出在抽样调查实践中经常会遇到多目标抽样问题 ,包括多目标变量问题和多目标总体问题。多目标变量问题 ,是指用一套样本估计多个目标变量的总量、均值、比率和比例等估计量时 ,由于各个变量的总体分布不一致 ,导致不同变量的估计量精度 (一般用相对误差表示 )不同 ,而且可能相差很大。在实践中 ,多目标变量问题是一个普遍问题 ,因为任何一项调查都不可能仅仅调查一个指标 (即变量 )。解决多目标变量问题的关键是在抽样设计中选择合适的辅助变量。一般来说 ,在抽样设计中作为分层辅助信息的变量的估计量精度会比其他变量的估计量…     

改进样本代表性的多目标追加平衡设计

本文主要讨论样本代表性的改进和多目标调查两个问题。一,本文提出了一种新的改进样本代表性多目标抽样方法,增加样本量与调整样本结构相结合的方法-追加样本的平衡设计,即通过追加样本,使得补充的样本与原来的样本组合生成新的平衡样本,相对于初始样本,减少样本与总体的结构性偏差。平衡样本是指辅助变量总量的霍维茨汤普森估计量等于总体总量真值。二,平衡样本通过选择与多个目标参数相关的辅助变量,使得一套样本对不同的目标参数而言都具有良好的代表性,进而完成多目标调查。结合2010年第六次人口分县普查数据,通过选择多个目标参数,对追加样本后的平衡样本作事后评估结果表明,追加平衡设计能够有效改进样本结构,使得样本结构与总体结构相近,降低目标估计的误差;同时也说明平衡抽样设计能够实现多目标调查,提高样本的使用效率。     

中国环保投资统计的多目标抽样设计

文章在考虑环保投资现实情况和抽样调查操作可行性的基础上,提出了环保投资分层三阶段抽样.根据环境污染治理投资额规模进行分层,三阶段均采取随机起点系统抽样或者PPS抽样.在全国样本中的样本量已调查的情况下,根据抽取的全部样本进行事后分层和适当追加样本量,对省级(行业、城市等层次)目标量进行估计,同时满足全国、部分典型行业、部分典型城市、部分省市等多目标要求的环保投资目标量估计.     

特征样本重复抽样建模方法和应用研究

本文在传统统计回归方法的基础上,构建了一种新的特征样本重复抽样回归(FSR)建模方法.该方法是依据变量特征采用机器抽样方法重复抽样,形成多个特征样本,然后对多个样本进行参数估计,形成参数的抽样分布;最后依据抽样分布,在多个优化目标要求下建立最优化模型.FSR方法能够作为社会科学研究中一种通用的建模方法.     

怎样合理利用辅助变量

一、什么是辅助变量 在抽样调查中,把变量按照其作用分为调查变量和辅助变量两种.调查变量指的是要估计的变量,如在农村经济抽样调查中,要估计粮食总产量、农村住房总收入等指标.辅助变量是指为提高调查变量的估计精度,在抽样或估计阶段引入的其他变量,比如,以农村住户作为抽样单位,以住户的粮食播种面积比例为抽取概率,实施PPS抽样,则粮食播种面积就是辅助变量.辅助变量是相对于调查变量而言的,在多目标抽样调查中,如果需要,一个调查变量可以作为另一个调查变量的辅助变量.     

用PPS单级整群样本估计总体的个体间方差

文章主要通过构思以PPS抽样方式抽取单级整群样本,在已用样本资料算出在某总体应用该抽样方案的设计效应的基础上,为推算下一个调查期对该总体依照该方案抽取样本时所需样本量的过程中,讨论如何用PPS单级整群样本来构造总体的个体间方差的无偏估计量的问题.     

基于倾向得分匹配与加权调整的非概率抽样统计推断方法研究

文章针对非概率抽样统计推断问题,提出了一种解决方法:首先采用倾向得分匹配选择样本,然后采用倾向得分逆加权、加权组调整和事后分层调整三种方法对匹配样本进行加权调整来估计目标总体,并比较不同方法估计的效果.蒙特卡罗模拟与实证研究表明:当网络访问固定样本大小与目标样本大小的比率小于3对,三种加权方法估计的效果均比未加权时匹配样本的估计效果好;当网络访问固定样本大小与目标样本大小的比率不小于3时,倾向得分事后分层调整与未加权的匹配样本估计效果较好.     

中国农产量调查中几种可行的PPS系统抽样设计

PPS系统抽样设计是国际上比较流行的设计,它在实践中有着广泛的应用。我国农产量调查采用的是对称等距抽样设计,它实际上可理解为是一种变形的PPS系统抽样设计,使用的是以播种面积作为辅助变量。文章给出了几种可行的PPS系统抽样设计,辅助变量分别取为农户数、播种面积、耕地面积、切块数,并对这几种抽样设计进行了应用分析。     

基于回归组合技术的连续性抽样估计方法研究

在使用样本轮换的连续性抽样调查中,不仅可以利用前期调查的研究变量的信息,还可使用现期调查的辅助变量信息来建立回归模型进行回归估计,进而构造回归组合估计量,并在此基础上确定最优样本轮换率和最优权重系数,使得回归组合估计量的方差最小,从而更大程度地提高连续性抽样调查的估计精度。     

Optimal PPS Sampling with Vanishing Auxiliary Variables – with Applications in Microscopy

Recently, non‐uniform sampling has been suggested in microscopy to increase efficiency. More precisely, proportional to size (PPS) sampling has been introduced, where the probability of sampling a unit in the population is proportional to the value of an auxiliary variable. In the microscopy application, the sampling units are fields of view, and the auxiliary variables are easily observed approximations to the variables of interest. Unfortunately, often some auxiliary variables vanish, that is, are zero‐valued. Consequently, part of the population is inaccessible in PPS sampling. We propose a modification of the design based on a stratification idea, for which an optimal solution can be found, using a model‐assisted approach. The new optimal design also applies to the case where ‘vanish’ refers to missing auxiliary variables and has independent interest in sampling theory. We verify robustness of the new approach by numerical results, and we use real data to illustrate the applicability.     

基于排序集样本和辅助变量中位数的均值比率估计方法

排序集抽样下利用辅助变量中位数构建了总体均值的改进比率估计模型,分析了该比率估计量的偏差和均方误差,并与简单随机抽样下的比率估计比较,证明了改进后的比率估计均方误差更小。以农作物播种面积和产量为研究对象进行实例分析,研究表明,基于排序集样本和辅助变量中位数的比率估计方法可以有效提高估计精度,验证了该构造方法的可行性。     

Optimal stratification in stratified designs using weibull-distributed auxiliary information

Sampling has evolved into a universally accepted approach for gathering information and data mining as it is widely accepted that a reasonably modest-sized sample can sufficiently characterize a much larger population. In stratified sampling designs, the whole population is divided into homogeneous strata in order to achieve higher precision in the estimation. This paper proposes an efficient method of constructing optimum stratum boundaries (OSB) and determining optimum sample size (OSS) for the survey variable. The survey variable may not be available in practice since the variable of interest is unavailable prior to conducting the survey. Thus, the method is based on the auxiliary variable which is usually readily available from past surveys. To illustrate the application as an example using a real data, the auxiliary variable considered for this problem follows Weibull distribution. The stratification problem is formulated as a Mathematical Programming Problem (MPP) that seeks minimization of the variance of the estimated population parameter under Neyman allocation. The solution procedure employs the dynamic programming technique, which results in substantial gains in the precision of the estimates of the population characteristics.     

Two-step calibration of design weights under two auxiliary variables in sample survey

Calibration on the available auxiliary variables is widely used to increase the precision of the estimates of parameters. Singh and Sedory [Two-step calibration of design weights in survey sampling. Commun Stat Theory Methods. 2016;45(12):3510–3523.] considered the problem of calibration of design weights under two-step for single auxiliary variable. For a given sample, design weights and calibrated weights are set proportional to each other, in the first step. While, in the second step, the value of proportionality constant is determined on the basis of objectives of individual investigator/user for, for example, to get minimum mean squared error or reduction of bias. In this paper, we have suggested to use two auxiliary variables for two-step calibration of the design weights and compared the results with single auxiliary variable for different sample sizes based on simulated and real-life data set. The simulated and real-life application results show that two-auxiliary variables based two-step calibration estimator outperforms the estimator under single auxiliary variable in terms of minimum mean squared error.     

Improvement in Estimating the Population Median in Simple Random Sampling and Stratified Random Sampling Using Auxiliary Information

This paper deals with estimation of population median in simple and stratified random samplings by using auxiliary information. Auxiliary information is rarely used in estimating population median, although there have been many studies to estimate population mean using auxiliary information. In this study, we suggest some estimators using auxiliary information such as mode and range of an auxiliary variable and correlation coefficient. We also expand these estimators to stratified random sampling for combined and separate estimators. We obtain mean square error equations for all proposed estimators and find theoretical conditions. These conditions are also supported by using numerical examples.     

高相关性辅助变量择优回归插补法

调查数据无回答在抽样调查中经常出现.无回答项目插补法是处理无回答的最主要方法之一,而辅助变量对提高插补值准确度非常重要.因此,研究调查数据无回答项目的高相关性辅助变量择优回归插补法,先筛选与目标变量间相关系数高的辅助变量,再建立回归插补模型.该方法的辅助变量选择过程简单,插补值准确性高.模拟例子演示了该方法的优良性.     

Environmental sampling with a concomitant variable: A comparison between ranked set sampling and stratified simple random sampling

In many environmental sampling situations, the variable of interest is either not easily observable or is too expensive to observe. Under such circumstances, the need arises to observe another variable, related to the variable of interest, so as to estimate the population parameters of interest. We study the performance of two different sampling procedures, i.e. ranked set sampling and stratified simple random sampling, when both stratification and ranking are accomplished on the basis of such a concomitant variable. The relative precision of the two methods is obtained and expressed as a function of population variance, between-stratum and between-rank variation, and the correlation coefficient between the variable of interest and the concomitant variable. The relative precision is computed for several important families of distributions that occur frequently in environmental and ecological work. Under equal allocation of sampling units, stratified simple random sampling is found to perform better than ranked set sampling, when the costs incurred to obtain sample measurements are ignored. When optimum allocation is considered for both methods, ranked set sampling performs better than stratified simple random sampling, when the concomitant variable is not highly correlated with the variable of interest. Furthermore, when the costs of sampling and the costs of measurement are incorporated into the assessment of the relative precision, the ranked set sampling is seen to be more efficient than stratified simple random sampling, particularly when the cost of stratification is high compared with that of ranking. This is generally the case in practice.     

Designing stratified sampling in economic and business surveys

In most economic and business surveys, the target variables (e.g. turnover of enterprises, income of households, etc.) commonly resemble skewed distributions with many small and few large units. In such surveys, if a stratified sampling technique is used as a method of sampling and estimation, the convenient way of stratification such as the use of demographical variables (e.g. gender, socioeconomic class, geographical region, religion, ethnicity, etc.) or other natural criteria, which is widely practiced in economic surveys, may fail to form homogeneous strata and is not much useful in order to increase the precision of the estimates of variables of interest. In this paper, a stratified sampling design for economic surveys based on auxiliary information has been developed, which can be used for constructing optimum stratification and determining optimum sample allocation to maximize the precision in estimate.