长沙市规模以下零售业抽样调查方案

本文介绍了一个根据具体城市情况制定的零售贸易餐饮业的抽样调查方案.以长沙市规模以下零售业、餐饮业为总体,用区域抽样代替一般的名录抽样,并采用细致的分层解决商业单位分布不均匀问题.文中也给出了与抽样方案配套的总体和域目标量的估计及相应的方差估计公式.     

基于辅助信息的分层排序集抽样及调查费用分析

辅助信息在改进和完善抽样设计、提高抽样估计精度和节省抽样费用等方面具有重要作用,鉴于此,基于分层排序集样本建立了总体均值的比率估计量,同时考虑估计精度和调查费用两个方面,证明了抽样方案的优良性.最后,通过实例进一步分析,结果表明,在给定的估计精度下,分层排序集抽样方法可以有效降低抽样调查费用.     

基于辅助信息的分层排序集抽样及调查费用分析北大核心

辅助信息在改进和完善抽样设计、提高抽样估计精度和节省抽样费用等方面具有重要作用,鉴于此,基于分层排序集样本建立了总体均值的比率估计量,同时考虑估计精度和调查费用两个方面,证明了抽样方案的优良性.最后,通过实例进一步分析,结果表明,在给定的估计精度下,分层排序集抽样方法可以有效降低抽样调查费用.     

等距抽样总体指标估计值的优良判定

等距抽样是实践中常用的一种抽样方法，它是事先将全区及总体各单位按某一标志排列，然后依固定顺序和间隔来抽选调查单位，以样本来推断总体．在实践中常用“随机确定起点，对称等距抽样”、“半距起点，顺序等距抽样”的方法．近几年，学术界对样本指标的推断研究的较多，但对这一抽样方法总体指标的估计却很少问究，致使实践中的估计方法较单一，抽样效果难以提高。本文想就标志等距抽样总体指标值的估计进行分析．为实际中的抽样技术提供理论上的数学依据。     

关于对照总体的总量的偏差的抽样检验方法

为估计一个未知有限总体全部个体之和,本文构造了一个对照总体,并以其全部个体之和作为估计量.在应用上,要求估计量对被估计量的相对偏差不超过某一给定标准,即接受原则,在此原则下文中给出抽样检验方案的解.     

对称分布下中位数排序集样本均值的有效性证明

总体均值的估计在整个抽样理论里扮演着无可争议的主角,而估计的好坏很大程度上依赖于抽样设计.文章从理论上证明了在对称分布下,基于中位数排序集抽样的样本均值作为总体均值的估计比简单随机抽样下样本均值作为总体均值的估计更有效.小样本情形下的两个数值例子和一个实例进一步验证了这个理论结果.     

多阶连续抽样设计下广义加权回归估计方法研究

在抽样调查领域中,关于抽样方案设计的研究应用较为充分和完整,但关于抽样估计的研究应用却较为缺乏和滞后。本文首先总结了国外相关研究成果,研究了基于广义加权回归的抽样估计方法,同时证明其满足渐近设计无偏和最小化渐近期望方差的理论条件。同时,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回归模型进行扩展,基于复杂的多阶连续抽样调查,建立各种类型的超总体回归模型进行模型辅助的广义加权回归抽样估计,给出了具体的回归估计步骤和结果,最终形成一套关于广义加权回归抽样估计的理论方法体系,为抽样估计方法在我国政府统计部门中的有效应用奠定理论基础。     

超总体模型下有限总体的估计

超总体模型是抽样理论与统计学其它分支联系的桥梁,借助于超总体模型研究抽样理论是一个有前途的方法．本文综述了这方面的结果,包括总体目标量的估计及其精度估计,同时提出了若干未来的研究问题．     

集团抽样不等概方法的均值估计及其性质

在集团抽样调查中，［１］和［２］给出了一般抽样方法及均值估计和它性质的论述，但未考虑各集团在总体中的地位对估计结果的影响．本文给出了集团抽样的不等概方法和均值的无偏估计，同时也给出了这种估计的方差及方差的无偏估计．     

集团抽样不等概方法的均值估计及其性质

在集团抽样调查中，[1]和[2]给出了一般抽样方法及均值估计和它性质的论述，但未考虑各集团在总体中的地位对估计结果的影响，本文给出了集团抽样的不等概方法和均值的无偏估计，同时也给出了这种估计的方差及方差的无偏估计。     

Confident estimation for density of a biological population based on line transect sampling

Line transect sampling is a very useful method in survey of wildlife population. Confident interval estimation for density D of a biological population is proposed based on a sequential design. The survey area is occupied by the population whose size is unknown. A stopping rule is proposed by a kernel-based estimator of density function of the perpendicular data at a distance. With this stopping rule, we construct several confidence intervals for D by difference procedures. Some bias reduction techniques are used to modify the confidence intervals. These intervals provide the desired coverage probability as the bandwidth in the stopping rule approaches zero. A simulation study is also given to illustrate the performance of this proposed sequential kernel procedure.     

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??How to solve the inference problem of candidate database web surveys is an urgent problem to be solved in the development of web survey. In order to solve this problem, the inference method of non-probability sampling based on superpopulation pseudo design and the combined sample is proposed. A superpopulation model is firstly built up to construct pseudo weights for a survey sample of the web candidate database. The estimator of the population mean is then computed according to the combined sample composed of the survey sample of the web candidate database and a probability sample. The variance estimator of the population mean estimator is lastly derived according to the variance estimation theory of the superpopulation model. The Bootstrap and Jackknife methods are also used to compute the variance estimator. And all these variance estimation methods are compared. The research results show that the population mean estimator based on superpopulation pseudo design and the combined sample is better, and has higher efficiency than the estimator only using the probability sample and the weighted estimator only using the survey sample of the web candidate database. The variance estimator computed by using the VM1, VM2 and VM3 method are relatively better.     

Research on Inference of Candidate Database Web Surveys Based on Superpopulation Pseudo Design and the Combined Sample

How to solve the inference problem of candidate database web surveys is an urgent problem to be solved in the development of web survey. In order to solve this problem, the inference method of non-probability sampling based on superpopulation pseudo design and the combined sample is proposed. A superpopulation model is firstly built up to construct pseudo weights for a survey sample of the web candidate database. The estimator of the population mean is then computed according to the combined sample composed of the survey sample of the web candidate database and a probability sample. The variance estimator of the population mean estimator is lastly derived according to the variance estimation theory of the superpopulation model. The Bootstrap and Jackknife methods are also used to compute the variance estimator. And all these variance estimation methods are compared. The research results show that the population mean estimator based on superpopulation pseudo design and the combined sample is better, and has higher efficiency than the estimator only using the probability sample and the weighted estimator only using the survey sample of the web candidate database. The variance estimator computed by using the VM1, VM2 and VM3 method are relatively better.     

论双系统估计量的无偏性

人口普查质量评估中所使用的双系统估计量是否为无偏估计量是一个很值得深入讨论的问题。只有无偏,才能确保使用双系统估计量估计的目标总体实际人数及人口普查净误差平均等于它们的实际数。针对人口普查质量评估工作中所使用的双系统估计量,论证这个估计量的无偏性条件。采用从既定假设出发进行推演的路径论证。研究结果表明,双系统估计量是目标总体实际人数无偏估计量的必要但非充分的条件是,人口普查与其质量评估调查相互独立以及目标总体中的每一个人在人口普查中的登记概率相同,在质量评估调查中登记的概率也相同。     

Bootstrap estimation of the asymptotic variances of statistical functionals

A modified bootstrap estimator of the asymptotic variance of a statistical functional is studied. The modified bootstrap variance estimator circumvents the problem of the original bootstrap when the population distribution has heavy tails, and requires less stringent conditions for its consistency than the ordinary bootstrap variance estimator. The consistency of the modified bootstrap variance estimator is established for differentiable statistical functionals.     

On Bayes and unbiased estimators of loss

We consider estimation of loss for generalized Bayes or pseudo-Bayes estimators of a multivariate normal mean vector, θ. In 3 and higher dimensions, the MLEX is UMVUE and minimax but is inadmissible. It is dominated by the James-Stein estimator and by many others. Johnstone (1988, On inadmissibility of some unbiased estimates of loss,Statistical Decision Theory and Related Topics, IV (eds. S. S. Gupta and J. O. Berger), Vol. 1, 361–379, Springer, New York) considered the estimation of loss for the usual estimatorX and the James-Stein estimator. He found improvements over the Stein unbiased estimator of risk. In this paper, for a generalized Bayes point estimator of θ, we compare generalized Bayes estimators to unbiased estimators of loss. We find, somewhat surprisingly, that the unbiased estimator often dominates the corresponding generalized Bayes estimator of loss for priors which give minimax estimators in the original point estimation problem. In particular, we give a class of priors for which the generalized Bayes estimator of θ is admissible and minimax but for which the unbiased estimator of loss dominates the generalized Bayes estimator of loss. We also give a general inadmissibility result for a generalized Bayes estimator of loss. Research supported by NSF Grant DMS-97-04524.     

Asymptotic Normality of a Combined Regression Estimator

In this paper, we propose a combined regression estimator by using a parametric estimator and a nonparametric estimator of the regression function. The asymptotic distribution of this estimator is obtained for cases where the parametric regression model is correct, incorrect, and approximately correct. These distributional results imply that the combined estimator is superior to the kernel estimator in the sense that it can never do worse than the kernel estimator in terms of convergence rate and it has the same convergence rate as the parametric estimator in the case where the parametric model is correct. Unlike the parametric estimator, the combined estimator is robust to model misspecification. In addition, we also establish the asymptotic distribution of the estimator of the weight given to the parametric estimator in constructing the combined estimator. This can be used to construct consistent tests for the parametric regression model used to form the combined estimator.     

A general ratio estimator and its application in model based inference

A general ratio estimator of a population total is proposed as an approximation to the estimator introduced by Srivastava (1985,Bull. Internat. Statist. Inst.,51(10.3), 1–16). This estimator incorporates additional information gathered during the survey in a new way. Statistical properties of the general ratio estimator are given and its relationship to the estimator proposed by Srivastava is explored. A special kind of general ratio estimator is suggested and it turns out to be very efficient in a simulation study when compared to several other commonly used estimators.The work of this author was supported by AFOSR grant #830080.     

利用样本分位数的Logistic总体分布参数的近似最佳线性无偏估计

Harter H_L.,Balakrishnan N.等先后讨论了Logistic总体分布参数的极大似然估计,近似极大似然估计;其后Ogawa J.,Lloyd E.H.,Kulldorff G.,Gupta S.S,及chan L.K. 等又先后讨论了Logistlic分布参数的最佳线性无偏估计及估计的相对效率等问题.令人遗憾的是:在大样本情形下,上述估计均难以求得.为缓解这一困难,本文讨论利用样本分位数的Logistic总体的近似最佳线性无偏估计,给出估计量的大样本性质,以及样本分位数不超过10情形下,估计量有渐近最大相对估计效率时样本分位数的选取方案等.