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。     

不同标准差估计方法下过程能力指数估计量的比较研究

估计量是统计推断的基础,通常无偏性是对一个好的估计量的基本要求。通过严格的数学推导,证明人们现在提出的过程能力指数（Cp）的估计量都是有偏的,且都有高估Cp的倾向;之后构造了Cp的两个无偏估计量;探讨了这两个无偏估计量的估计效率;最后通过试算和比较,认为当样本容量n较大,同时估计精度又不要求太高时,可直接使用p作为Cp的估计量;但在样本容量较小,或者对估计精度要求很高的场合,则必须选择p（D）作为Cp的估计量。     

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。     

改进的汉森-赫维茨估计量及其应用

本文利用阈值截断方法对经典的汉森-赫维茨估计进行修正,提出改进的汉森-赫维茨估计量（简称IHH估计量）。理论上,本文证明了IHH估计量的相合性,渐近无偏性和渐近正态性,并给出了IHH估计量的均方误差及其无偏估计。此外,基于IHH估计量,本文分别对分层抽样和二阶抽样下的有限总体估计进行改进。为说明所提出方法的有效性,本文比较了所有改进估计量和传统估计量的均方误差。最后,数值分析进一步说明本文提出的估计量具有更高的精度。     

PPS抽样下比率估计量的构建及应用

在PPS抽样的估计阶段为提高估计精度,利用比率辅助信息分别构建比率HH和比率HT估计量,并对这两种新估计量的性质进行研究,推导得到了估计量均方误差的近似表达式。同时,结合实例进行对比分析,展示了新估计量的优良性质。     

人口普查遗漏的组合式估计方法

以人口普查标准时点上的人口总体(全国、全国以下各行政区)为研究对象,研究如何利用对总体登记的普查人口名单和覆盖调查人口名单构造的组合式遗漏估计量估计人口普查遗漏⁽(1))人口数。目前各国政府统计部门在人口普查遗漏估计中主要使用单重遗漏估计量,但该方法未包括同时遗漏于这两份调查人口名单的双重遗漏人口,低估了总体普查遗漏人口数。组合式遗漏估计量除了包括单重遗漏人口外,还包括了双重遗漏人口,避免了单重遗漏估计量的缺陷。采取抽样估计和数理模型相结合的方法研究单重遗漏估计量、双重遗漏估计量和组合式遗漏估计量,理论和数据分析结果表明：组合式遗漏估计量在抽样估计精度和遗漏人口数估计上优于单重遗漏估计量,尤其是在分析双重遗漏人口特征和改进未来人口普查登记工作等方面;在使用刀切法近似计算总体的组合式遗漏估计量的抽样方差时,不能忽视等概率人口层之间的协方差,否则可能低估或高估其抽样方差;单重遗漏估计量和组合式遗漏估计量属于有偏估计量,要使用均方误差比较其估计精度。本文创新之处在于,利用原始数据全面演示了单重遗漏估计量、双重遗漏估计量和组合式遗漏估计量及其抽样方差估计量等的详细计算过程...     

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。     

基于组合式三系统估计量的人口普查净误差估计

研究组合式三系统估计量,旨在为政府统计部门设计人口普查净误差估计方案提供重要参考,提高净误差估计精度。研究对象是人口普查标准时点上的人口总体。利用对这个总体登记的普查人口名单、事后计数调查人口名单和行政记录人口名单构造的组合式三系统估计量,解决当今人口普查净误差估计前沿方法的普通三系统估计量所存在的问题。这些问题包括未考虑到不同信息的重要性,以及在使用之前还要进行复杂的检验或选择。为实现这一目标,采取数理模型方法研究组合式三系统估计量及其与之相关的抽样及估计问题。研究结果表明：组合式三系统估计量须在等概率人口层建立,分层刀切法适合于近似计算其抽样方差;事后计数调查人口的构成方法影响组合式三系统估计量的估计精度。采用A构成法建立的组合式三系统估计量在抽样估计精度方面优于使用B构成法构造的组合式三系统估计量;在计算组合式三系统估计量估计的总体实际人口数的抽样方差时,不能忽视协方差的存在,否则低估或高估总体的抽样方差。研究创新是提出抽样登记的、人口移动的组合式三系统估计量。研究价值在于,将目前的普通三系统估计量拓展到组合式三系统估计量,丰富了三系统估计量理论,拓宽了人们研究三系统估计量的视野...     

半参数空间变系数回归模型的两步估计方法及其数值模拟

文章提出了关于半参数空间变系数回归模型的两步估计方法,该方法可得到模型中常值系数估计量的精确解析表达式,广泛的数值模拟表明所提出的估计方法对估计常值系数具有满意的精度和稳定性。     

非等间隔动态面板数据模型：基于半差分的估计方法和应用

本文提出了非等间隔动态面板数据模型的估计方法,包括非线性最小二乘和最短距离估计法以及这两种估计方法的一步估计量,并且证明了这几个估计量的一致性和渐进正态性。我们使用数值模拟的方法验证了这些估计在有限样本中的估计精度,并且将这四种估计方法应用于实际的问题当中,最终得到了与以往的文献基本一致的估计结果。     

New calibration estimator based on two auxiliary variables in stratified sampling

In recent years, calibration estimation has become an important field of research in survey sampling. This paper proposes a new calibration estimator for the population mean in the presence of two auxiliary variables in stratified sampling. The theory of new calibration estimator is given and optimum calibration weights are derived. A simulation study is carried out to performance of the proposed calibration estimator over other existing calibration estimators. The results reveal that the proposed calibration estimators are more efficient than other existing calibration estimators in stratified sampling.     

Spatial estimation and rescaled spatial bootstrap approach for finite population

In this study, an attempt has been made to improve the sampling strategy incorporating spatial dependency at estimation stage considering usual aerial sampling scheme, such as simple random sampling, when the underlying population is finite and spatial in nature. Using the distances between spatial units, an improved method of estimation, viz. spatial estimation procedure, has been proposed for the estimation of finite population mean. Further, rescaled spatial bootstrap (RSB) methods have been proposed for approximately unbiased estimation of variance of the proposed spatial estimator (SE). The properties of the proposed SE and its corresponding RSB methods were studied empirically through simulation.     

Use of several auxiliary variables in estimating the population mean in a two-occasion rotation pattern

ABSTRACT

This paper addresses the problem of estimation of the population mean on the current (second) occasion in two-occasion successive sampling. Utilizing the readily available information on several auxiliary variables on both occasions and the information on the study variable from the previous occasion, an estimation procedure of the population mean on the current occasion has been proposed. Theoretical properties of the proposed estimator have been investigated. Optimum replacement policy to the proposed estimator has been discussed. The proposed estimator has been compared empirically with the sample mean estimator, when there is no matching and the optimum estimator which is a linear combination of the means of the matched and unmatched portions of the sample at the current occasion. Appropriate recommendations have been made for practical applications.     

A new estimator combining the ridge regression and the restricted least squares methods of estimation

It is well-known in the literature on multicollinearity that one of the major consequences of multicollinearity on the ordinary least squares estimator is that the estimator produces large sampling variances, which in turn might inappropriately lead to exclusion of otherwise significant coefficients from the model. To circumvent this problem, two accepted estimation procedures which are often suggested are the restricted least squares method and the ridge regression method. While the former leads to a reduction in the sampling variance of the estimator, the later ensures a smaller mean square error value for the estimator. In this paper we have proposed a new estimator which is based on a criterion that combines the ideas underlying these two estimators. The standard properties of this new estimator have been studied in the paper. It has also been shown that this estimator is superior to both the restricted least squares as well as the ordinary ridge regression estimators by the criterion of mean sauare error of the estimator of the regression coefficients when the restrictions are indeed correct. The conditions for superiority of this estimator over the other two have also been derived for the situation when the restrictions are not correct.     

Utilization of higher order moments of scrambling variables in randomized response sampling

In this paper, a new estimator for estimating the proportion of a potentially sensitive attribute in survey sampling has been introduced. The proposed estimator makes use of higher order moments of the scrambling variable at the estimation stage. The proposed estimator has been found to be more efficient than the estimator due to Kuk [1990. Asking sensitive questions indirectly. Biomerika 77(2), 436–438] and Franklin [1989. A comparison of estimators for randomized response sampling with continuous distributions from a dichotomous population. Comm. Statist. Theory Methods 18, 489–505] type estimators in randomized response sampling. Recently, Guerriero and Sandri [2007. A note on the comparison of some randomized response procedures. J. Statist. Plann. Inference 137, 2184–2190] have shown that the family of randomized response models proposed by Kuk [1990. Asking sensitive questions indirectly. Biomerika 77(2), 436–438] is better than the Simmons’ family in terms of efficiency and protection.     

An estimator of a conditional quantile in the presence of auxiliary information

In this paper, a new estimator for a conditional quantile is proposed by using the empirical likelihood method and local linear fitting when some auxiliary information is available. The asymptotic normality of the estimator at both boundary and interior points is established. It is shown that the asymptotic variance of the proposed estimator is smaller than those of the usual kernel estimators at interior points, and that the proposed estimator has the desired sampling properties at both boundary and interior points. Therefore, no boundary modifications are required in our estimation.     

A jackknife variance estimator for unequal probability sampling

Summary.  The jackknife method is often used for variance estimation in sample surveys but has only been developed for a limited class of sampling designs. We propose a jackknife variance estimator which is defined for any without-replacement unequal probability sampling design. We demonstrate design consistency of this estimator for a broad class of point estimators. A Monte Carlo study shows how the proposed estimator may improve on existing estimators.     

An alternative to ratio estimator in post-stratification

This article proposes an alternative to usual ratio estimator of population mean in post-stratified sampling procedure and its properties are analyzed. Both theoretical and empirical findings are encouraging and support the soundness of the proposed procedure for mean estimation over an alternative to ratio estimator in simple random sampling without replacement suggested by Srivenkataramana and Tracy (1980), usual combined ratio estimators suggested by Ige and Tripathi (1989), and usual unbiased estimator in post-stratified sampling scheme. Both theoretical and empirical findings are encouraging and support the soundness of the present study. At the end, a simulation study has been carried out to verify the superiority of the proposed estimator.     

Nonparametric estimation of the entropy using a ranked set sample

This article is concerned with nonparametric estimation of the entropy in ranked set sampling. Theoretical properties of the proposed estimator are studied. The proposed estimator is compared with the rival estimator in simple random sampling. The applications of the proposed estimator to the mutual information estimation as well as estimation of the Kullback–Leibler divergence are provided. Several Monté-Carlo simulation studies are conducted to examine the performance of the estimator. The results are applied to the longleaf pine (Pinus palustris) trees and the body fat percentage datasets to illustrate applicability of theoretical results.     

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。