住户调查中代表性样本的一种探索获取方法——平衡抽样设计

住户调查是我国社会经济统计调查体系的重要组成部分,样本代表性直接决定统计数据质量。多阶段抽样中初级单元的方差对估计的影响是主要的,因此本文结合2010年全国第六次人口普查分县数据,采用平衡抽样设计获取初级单元的代表性样本-平衡样本。对代表性样本的事后评估结果表明,样本结构与总体结构吻合,目标估计的误差很小,说明了本文平衡设计的有效性。     

各级政府统计机构共用抽样样本问题研究

 目前,抽样调查已经成为我国政府统计机构收集统计数据最常用的方法之一。针对我国中央、省、市、县各级政府层层都具有管理经济的职能,各级政府都需要掌握本级和下一级政府管辖区域内的主要统计数据这一需求,以及我国现行行政管理体制、财政预算管理体制、政府统计管理体制等实际情况,本文从理论分析和模拟实例两个方面,从抽样调查的结果和成本两个角度,对我国政府统计机构开展抽样调查时,如何在现行体制的制约下,设计一套对各级政府行政管辖区域都具有代表性的公共抽样调查样本,以有效地展开工作进行了较为系统的研究。     

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。     

规模以下工业抽样调查中代表性样本的一种探索设计:平衡抽样设计

规下工业抽样调查是社会经济统计调查的重要组成部分,为国民经济核算提供基础数据,而样本代表性直接决定统计推断结果。对企业目录库抽取平衡样本,能够使得样本结构与总体结构相似。平衡样本是指满足如下条件的样本:辅助变量的汉森赫维茨估计等于总体总量真值。平衡抽样设计需要包含丰富辅助信息的完善抽样框,政府统计数据能够为此提供足够的支撑。基于2009年工业企业数据库的实证分析表明,平衡抽样设计对总体总量的估计相对误差很小,特别是估计的均值与总体真值非常接近,近似无偏;与简单随机抽样比较,平衡抽样设计更加有效。     

改进住户调查数据质量的多维视角

为了更好地提高住户统计数据的公信力,笔者结合自己从事社会调查研究与实践的经验和体会,认为应该从以下多个视角入手,改进现行住户调查数据的质量。一、提高样本的代表性是改进住户调查数据质量的基础编制符合实际的抽样框,是保证调查住户样本代表性的关键。城镇住户调     

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.     

统计数据质量监控和评估方法研究(下)

从探索建立上海市统计系统数据质量监控和评估体系方法角度出发,结合统计"四大工程"建设和一套表工作的实施,对统计数据质量监控的管理从统计设计、统计培训等五个阶段提出了工作要求和方法。目前统计数据质量评估的方法有历史数据对比分析、趋势性对比分析等。为进一步完善统计数据质量监控和评估体系,建议提高统计数据质量的监控和评估效果、建立健全下级统计机构的数据质量考核机制等。     

推行“MPPS”，促进农村统计工作上新台阶

“MPPS”即多目标与规模成比例的概率抽样。它是在“PPS”即与规模成比例的概率抽样的基础上,考虑了多种调查目标的因素抽取调查样本,以满足多种调查指标的需要。目前,广东已制定出《广东省农村统计多目标复合抽样调查方案》。该方案省市县三级样本兼容、多目标调查,能够满足多级政府管理、调查指标多样化的需要,是一种适合当前经济管理体制、满足统计调查目标需要的较为理想的抽样调查方法。 随着市场经济发展的不断深入,对农村统计的要求越来越高。从原来以产品产量为主,转变为以农产品价格、农业经济核算、农业生产投入和农民收入支出等指标为主。党的“十六”大提出全面建设小康社会的宏伟目标后,农村统计工作更要为建设小康社会服务,必须建立科学的小康监测指标。农民收入支出、农产品价格、农业生产投入等等靠全面报表方法收集统计数据,在目前的经济体制下是不可能做到的。只     

农村住户调查县级样本代表性评估方法研究

 全国农村住户调查县级样本在1984年抽选后几未改变,在全国农村住户新一轮样本轮换中对其代表性进行评估非常必要,因此研究开发县级样本代表性评估方法有重要意义。本文利用第二次全国农业普查和县市统计数据,以甘肃省为例,通过分析调查县样本特性、考察调查县的收入分布和地域分布,探索调查县农民收入水平对所在省农民收入水平代表性的评估与调查县调整方法。     

改变抽样方法 提升轮换层次——对改进城市住户调查样本轮换模式的思考

为了提高样本对总体的代表性，同时保证前后期样本数据的可比性和衔接性，从20世纪90年代开始，我国的城市住户调查主要采用样本轮换的方法来确定报告期的样本。如今，样本轮换的优点已得到了充分的验证，但是由于目前样本轮换模式存在一定的问题，使得轮换的效果大打折扣。目前城市住户调查中的抽样和样本轮换方法我国城市住户调查，首先在全国抽选了226个市县作为样本的初级抽样单位。然后在各初级单位中，每三年进行一次居民家庭基本情况的一次性调查，并依此为抽样框，抽取部分住户组成经常性调查户，以户为统计单位进行常年调查。（一…     

The Australian Census Longitudinal Dataset: using record linkage to create a longitudinal sample from a series of cross‐sections

The Australian Bureau of Statistics is creating a longitudinal sample, called the Australian Census Longitudinal Dataset (ACLD), by linking person records across its five‐yearly Census of Population and Housing. This paper proposes a Multi‐Panel framework for selecting and weighting records in the ACLD. This framework can be applied more generally to selecting longitudinal samples from a series of cross‐sectional administrative files. The proposed framework avoids some significant limitations of the popular ‘Top‐Up’ sampling approach to maintaining the cross‐sectional and longitudinal representativeness of a sample over time.     

An omnibus two-sample test for ranked-set sampling data

We develop an omnibus two-sample test for ranked-set sampling (RSS) data. The test statistic is the conditional probability of seeing the observed sequence of ranks in the combined sample, given the observed sequences within the separate samples. We compare the test to existing tests under perfect rankings, finding that it can outperform existing tests in terms of power, particularly when the set size is large. The test does not maintain its level under imperfect rankings. However, one can create a permutation version of the test that is comparable in power to the basic test under perfect rankings and also maintains its level under imperfect rankings. Both tests extend naturally to judgment post-stratification, unbalanced RSS, and even RSS with multiple set sizes. Interestingly, the tests have no simple random sampling analog.     

Increased Fisher’s information for parameters of association in count regression via extreme ranks

The article details a sampling scheme which can lead to a reduction in sample size and cost in clinical and epidemiological studies of association between a count outcome and risk factor. We show that inference in two common generalized linear models for count data, Poisson and negative binomial regression, is improved by using a ranked auxiliary covariate, which guides the sampling procedure. This type of sampling has typically been used to improve inference on a population mean. The novelty of the current work is its extension to log-linear models and derivations showing that the sampling technique results in an increase in information as compared to simple random sampling. Specifically, we show that under the proposed sampling strategy the maximum likelihood estimate of the risk factor’s coefficient is improved through an increase in the Fisher’s information. A simulation study is performed to compare the mean squared error, bias, variance, and power of the sampling routine with simple random sampling under various data-generating scenarios. We also illustrate the merits of the sampling scheme on a real data set from a clinical setting of males with chronic obstructive pulmonary disease. Empirical results from the simulation study and data analysis coincide with the theoretical derivations, suggesting that a significant reduction in sample size, and hence study cost, can be realized while achieving the same precision as a simple random sample.     

国家抽样调查县的代表性问题研究

中国在1984年确定了国家抽样调查县,这些调查县一直使用至今,已有20多年。利用这些调查县,中国进行了许多关于农业的调查,并且有一些原因一直没有改变这些调查县。近年来,有人对这些调查县的代表性提出疑问,是可以理解的。我们应该解释这些调查县能被保持不变的原因,并能找出改进调查的好办法。本文给出一种方法,就是使用调整系数来解决这些调查县的代表性问题,并且利用第一次中国农业普查的数据来对方法进行模拟,得到了较好的结果。我国2007年正在进行第二次全国农业普查,我们可以应用普查结果来完善抽样调查,提高国家抽样调查县的代表性。我们建议结合面积抽样框进行多样框抽样设计,并可以考虑以县作为中国农业调查的初级抽样单元。     

中国广义回归抽样估计系统的构建及应用

在抽样理论和应用研究方面,中国一直比较重视抽样方案设计,而忽视抽样估计方法研究。本文在系统总结加拿大等西方国家成功经验的基础上,引入并改进了一套广义回归估计系统,应用在复杂的连续多阶抽样调查中。本文以各类常见的抽样设计为基础,通过模型组和模型水平将现有的超总体模型进行扩展,建立各种类型的回归模型进行模型辅助的广义回归估计,最终形成一套广义回归估计系统,为中国抽样估计的应用研究奠定理论基础。最后,本文以中国农产量的连续多阶抽样调查为例,给出了具体的回归估计程序,从而验证这套系统的实践性和应用价值。     

Information content of partially rank-ordered set samples

Partially rank-ordered set (PROS) sampling is a generalization of ranked set sampling in which rankers are not required to fully rank the sampling units in each set, hence having more flexibility to perform the necessary judgemental ranking process. The PROS sampling has a wide range of applications in different fields ranging from environmental and ecological studies to medical research and it has been shown to be superior over ranked set sampling and simple random sampling for estimating the population mean. We study Fisher information content and uncertainty structure of the PROS samples and compare them with those of simple random sample (SRS) and ranked set sample (RSS) counterparts of the same size from the underlying population. We study uncertainty structure in terms of the Shannon entropy, Rényi entropy and Kullback–Leibler (KL) discrimination measures.     

Some efficient random imputation methods

Imputation methods that assign a selection of respondents’ values for missing i tern nonresponses give rise to an addd,tional source of sampling variation, which we term imputation varLance , We examine the effect of imputation variance on the precision of the mean, and propose four procedures for sampling the rEespondents that reduce this additional variance. Two of the procedures employ improved sample designs through selection of respc,ndents by sampling without replacement and by stratified sampl;lng. The other two increase the sample base by the use of multiple imputations.     

Weighted analyses for cohort sampling designs

Weighted analysis methods are considered for cohort sampling designs that allow subsampling of both cases and non-cases, but with cases generally sampled more intensively. The methods fit into the general framework for the analysis of survey sampling designs considered by Lin (Biometrika 87:37–47, 2000). Details are given for applying the general methodology in this setting. In addition to considering proportional hazards regression, methods for evaluating the representativeness of the sample and for estimating event-free probabilities are given. In a small simulation study, the one-sample cumulative hazard estimator and its variance estimator were found to be nearly unbiased, but the true coverage probabilities of confidence intervals computed from these sometimes deviated significantly from the nominal levels. Methods for cross-validation and for bootstrap resampling, which take into account the dependencies in the sample, are also considered. An erratum to this article can be found at     

Estimation of an indicator of the representativeness of survey response

Nonresponse is a major source of estimation error in sample surveys. The response rate is widely used to measure survey quality associated with nonresponse, but is inadequate as an indicator because of its limited relation with nonresponse bias. Schouten et al. (2009) proposed an alternative indicator, which they refer to as an indicator of representativeness or R-indicator. This indicator measures the variability of the probabilities of response for units in the population. This paper develops methods for the estimation of this R-indicator assuming that values of a set of auxiliary variables are observed for both respondents and nonrespondents. We propose bias adjustments to the point estimator proposed by Schouten et al. (2009) and demonstrate the effectiveness of this adjustment in a simulation study where it is shown that the method is valid, especially for smaller sample sizes. We also propose linearization variance estimators which avoid the need for computer-intensive replication methods and show good coverage in the simulation study even when models are not fully specified. The use of the proposed procedures is also illustrated in an application to two business surveys at Statistics Netherlands.     

Robust extreme ranked set sampling

In this paper, a robust extreme ranked set sampling (RERSS) procedure for estimating the population mean is introduced. It is shown that the proposed method gives an unbiased estimator with smaller variance, provided the underlying distribution is symmetric. However, for asymmetric distributions a weighted mean is given, where the optimal weights are computed by using Shannon's entropy. The performance of the population mean estimator is discussed along with its properties. Monte Carlo simulations are used to demonstrate the performance of the RERSS estimator relative to the simple random sample (SRS), ranked set sampling (RSS) and extreme ranked set sampling (ERSS) estimators. The results indicate that the proposed estimator is more efficient than the estimators based on the traditional sampling methods.