多水平样本轮换调查及其组合估计方法

文章以两水平样本轮换为例,研究了连续性抽样调查中多水平样本轮换的问题.在多水平样本轮换模式下,不仅能够搜集固定的轮换样本在连续各期的信息,而且能够每一期更换新的轮换样本.文章还运用组合估计的思想.利用现期及过去各期的调查信息,分别构造出了每月总体情况、月度之间以及年度之间变化情况的组合估计量,并推导出了各类组合估计量的方差.     

连续性抽样调查中总体均值的估计

在连续性抽样调查中,利用前期信息和辅助信息可以大大提高估计精度,但是以往的估计量大多假设辅助信息总体均值已知,文章介绍一种在连续性抽样调查中,辅助信息总体均值未知的情况下,通过两阶段抽样,利用轮换样本方法和辅助变量信息,对总体均值进行估计的新的估计方法,并将新提出的估计量与原有的估计量进行比较,发现其精度更高,而且有利于减少调查成本。     

基于卡尔曼滤波估计的连续性抽样调查研究

 针对连续性抽样调查中如何提高连续调查数据准确性的问题,本文引入时间序列分析方法,分别考虑连续性抽样调查中的重复样本和轮换样本等不同情况,建立了连续性抽样调查下的状态空间模型,利用成熟的卡尔曼滤波估计方法给出了总体均值的估计量。由于状态空间模型及卡尔曼滤波估计方法能够充分利用各期连续样本的调查信息,给出了精度更高的估计量,从而能够产生更加准确的连续性时间序列数据。     

双重抽样框下项目无回答插补估计方法研究

抽样调查工作中无回答情形不可避免,双重抽样框下亦如此,因此需要对双重抽样框下抽样调查项目无回答造成的估计量偏差进行纠偏校正。首先通过二重抽样获取辅助变量的信息,使用其构造比率估计量与比率型指数估计量的组合估计量对双重抽样框下抽样调查中项目无回答数据进行插补,得到对应各部分子总体的均值估计,再用Hartley估计量的形式对总体总值进行估计。通过计算估计量偏差、均方误差及最优权重系数,对比相同条件下完全回答时同类型组合估计量均方误差的相对精度损失与使用单一比率型指数估计量的相对精度损失,随机模拟结果显示损失率较低,插补方法有效。选择合适的辅助变量构造比率估计量和比率型指数估计量的组合估计量做插补值,更充分利用辅助变量和已回答研究变量信息,基于提出的组合估计量于抽样调查工作具有一定的应用价值。     

分层抽样下的样本轮换理论研究

讨论用于回归估计的二相抽样理论在分层抽样下样本轮换后估计量的构造及其精度问题，并在构造的估计量的基础上计算了分层抽样下的最优样本轮换率，这对于深入研究分层抽样理论，使其估计量的精度提高，从而更好地实现抽样调查的目标有积极意义。     

小微企业抽样调查的样本轮换

目前,小微企业抽样调查数据受到各级政府和社会各界的高度关注。针对小微企业单位新增、消亡变动频繁的特点,研究了总体单位及样本量变动的一般条件下的样本轮换理论,对样本轮换率和估计量进行了探讨,扩大了研究结果的适用范围,得到了简单随机抽样、分层抽样中样本轮换的有关结论,对估计量的抽样误差进行了有效控制,并进行了相关实证研究,最后提出了构造适合小微企业连续性抽样调查的样本轮换设计模式和方法。     

基于多重抽样框的连续性抽样估计方法研究

多重抽样框可以解决单一抽样框难以完整覆盖流动性目标总体的难题,连续性抽样调查则可以获取变量的时序观测数据,对总体现象进行追踪调查。本文将多重抽样框调查与连续性抽样调查两种方法结合在一起进行研究,深入分析基于多重抽样框的连续性抽样估计方法。文章首先设计了连续性调查环境下总体结构变动表;然后,在简单随机抽样假定下的轮换样本调查情形开展研究,设计了14种参数缩减方法对构建的似然函数进行估计求解,并给出了估计量的迭代计算过程;最后,对本文的研究内容进行了总结与展望。     

基于时间序列分析方法的连续性抽样调查研究

针对连续性抽样调查中如何利用过去各期的调查信息来提高现期抽样估计精度的问题，引入时间序列分析方法，分别考虑连续性抽样调查中重复样本和重叠样本等不同情况，建立了不同情况下的时间序列模型，利用成熟的时间序列分析方法给出了总体特征的线性组合估计量。由于时间序列分析方法能够充分利用以往各期的调查信息，从而能够给出精度更高的估计量。     

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.     

三维平衡多水平轮换设计及其连续性估计方法研究

 针对现行多水平轮换调查存在的一系列问题,本文提出三维平衡多水平轮换模式设计及其广义组合估计方法。在这套设计方法中,将各种偏差和两类相关关系引入到广义组合估计量公式中,提高了估计量的准确性,同时在多目标调查下还应用最优化方法确定一套最优系数,使得连续性的多目标调查的整体估计精度达到最高,同时又兼顾各类目标量的精度要求。这套系统化、理论化的设计方法将为我国未来开展多水平轮换调查提供理论指导。     

Search of Good Rotation Patterns to Improve the Precision of Estimates at Current Occasion

In rotation (successive) sampling, it is common practice to use the information collected on a previous occasion to improve the precision of the estimates at current occasion. The previous information may be in the form of an auxiliary character, the character under study itself, or both. In the present work, information on an auxiliary character, which is readily available on all the occasions, has been used along with the information on study character from the previous and current occasion. Consequently, chain type difference and regression estimators have been proposed for estimating the population mean at second (current) occasion in the two occasions rotation (successive) sampling. The proposed estimators have been compared with sample mean estimator when there is no matching and the optimum estimator, which is the combination of the means of the matched and unmatched portions of the sample at the second occasion. Optimum replacement policy is also discussed. Theoretical results have been justified through empirical interpretation.     

Preliminary test estimators in double sampling with two auxiliary variables

An attempt has been mads to suggest some estimators for population mean in double sampling with two auxiliary variables., alternative to the usual regression estimator. When the experimenter has partial Information about the mean of the auxiliary variable or variables, preliminary test estimators can be used. The bias, mean square error, relative efficiency and optimum allocation of sample sizes are obtained for the suggested estimators.     

Estimation of population mean based on dual use of auxiliary information in non response

Whenever there is auxiliary information available in any form, the researchers want to utilize it in the method of estimation to obtain the most efficient estimator. When there exists enough amount of correlation between the study and the auxiliary variables, and parallel to these associations, the ranks of the auxiliary variables are also correlated with the study variable, which can be used a valuable device for enhancing the precision of an estimator accordingly. This article addresses the problem of estimating the finite population mean that utilizes the complementary information in the presence of (i) the auxiliary variable and (ii) the ranks of the auxiliary variable for non response. We suggest an improved estimator for estimating the finite population mean using the auxiliary information in the presence of non response. Expressions for bias and mean squared error of considered estimators are derived up to the first order of approximation. The performance of estimators is compared theoretically and numerically. A numerical study is carried out to evaluate the performances of estimators. It is observed that the proposed estimator is more efficient than the usual sample mean and the regression estimators, and some other families of ratio and exponential type of estimators.     

Estimation of rare and clustered population variance in adaptive cluster sampling

Abstract

Many researchers used auxiliary information together with survey variable to improve the efficiency of population parameters like mean, variance, total and proportion. Ratio and regression estimation are the most commonly used methods that utilized auxiliary information in different ways to get the maximum benefits in the form of high precision of the estimators. Thompson first introduced the concept of Adaptive cluster sampling, which is an appropriate technique for collecting the samples from rare and clustered populations. In this article, a generalized exponential type estimator is proposed and its properties have been studied for the estimation of rare and highly clustered population variance using single auxiliary information. A numerical study is carried out on a real and artificial population to judge the performance of the proposed estimator over the competing estimators. It is shown that the proposed generalized exponential type estimator is more efficient than the adaptive and non adaptive estimators under conventional sampling design.     

基于多重抽样框的校准估计方法研究

在现代抽样调查中,校准估计方法能够通过有效利用辅助信息来提高估计量的精度,多重抽样框抽样调查则不仅可以解决单一抽样框覆盖不全的问题,还可以节约抽样设计阶段的成本。本文将这两种现代抽样估计与设计方法进行结合,将校准估计方法引入到基于多重抽样框的抽样调查体系中,在实现节约调查成本的同时,还能够提高估计量的精度。文章首先按照分离抽样框与组合抽样框估计方法的分类思路,对传统多重抽样框估计方法进行系统梳理;然后在最短距离法校准估计的分析框架下,按照调查时所能掌握辅助信息的具体情况,给出了两类多重抽样框估计情形下的各种不同形式的校准估计量;随后数值分析的比较结果也表明在多重抽样框中校准估计量的估计效率明显优于传统估计量;最后对本文研究进行总结的基础上,给出了我国抽样实践中应用这套先进抽样估计方法体系的展望。     

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.     

Estimation of mean in presence of non-response using two phase sampling scheme

This paper considers the problem of estimating the population mean using information on an auxiliary variable in presence of non-response. Some modified ratio, product and regression estimators in double sampling have been suggested and their properties are studied. It is shown that to the first degree of approximation, estimators based on estimated optimum values have the same variance as that of the optimum estimators. An empirical study is carried to judge the merits of the suggested estimators over conventional unbiased estimator and other known estimators. Both theoretical and empirical study results present the soundness and usefulness of the suggested estimators in practice.     

Some estimation procedures using a linear model in successive sampling

ABSTRACT

In successive sampling some recent works depict the use of super-population models where information on stable auxiliary variable over occasions has been utilized. Stability character of auxiliary variable may not sustain, if the duration between occasions is large. To cope with such situations, the present work is an attempt to develop some estimation procedures by utilizing the information on two independent auxiliary variables through a linear super-population model. Some estimators are proposed to estimate the current population mean in two occasions successive (rotation) sampling. Optimum replacement strategies are formulated and performances of the proposed estimators have been discussed. Results are interpreted through empirical studies.     

Estimation of population mean in two-occasion successive sampling

In the present article, we have proposed some classes of estimators based on transformed auxiliary variable. The biases and mean squared errors (MSEs) of the proposed estimators have been obtained. The proposed estimators have been compared with simple mean estimator when there is no matching and the optimum estimator, which is a combination of the means of the matched and unmatched portion of the sample at the second occasion. Optimum replacement policy and the efficiency of the proposed estimators have been discussed. Theoretical results are well supported with an empirical study.     

Some Effective Rotation Patterns in Estimation of Population Mean in Two-Occasion Successive Sampling

This article is an attempt to explore some effective rotation patterns in estimation of current population mean in two occasions successive sampling. Utilizing the readily available information on an auxiliary variable on both occasions and the information on study variable from the previous occasion, some efficient estimation procedures have been suggested. Optimum replacement strategies and the efficiencies of the proposed estimators have been discussed. Empirical studies are carried out and suitable recommendations are made.