PPS适应性整群抽样下估计量的研究

在群规模存在差异时,最有效的抽样方法是PPS整群抽样;在单元存在聚集性且总体信息不明确时,使用PPS适应性整群抽样是最有效的.利用PPS适应性整群抽样设计中的样本外推机制产生的样本信息,对HH和HT估计进行调整,提出修正的HH和HT估计,并对估计量的性质进行了研究.     

PPS整群抽样下的比估计量

本文研究有辅助指标的ＰＰＳ整群抽样,构造了总体均值和总值的ＰＰＳ比估计量,证明ＰＰＳ比估计量是可用的,给出ＰＰＳ比估计量优于ＰＰＳ估计量的条件并讨论其优良性．     

分层整群抽样的Warner模型RRT技术及其对大学生婚前性行为调查中的应用

本文的目的为敏感性问题提供科学的较复杂抽样调查方法及其统计量的计算公式。使用Cochran W.G.的抽样理论、随机应答技术的Warner模型、全概率公式、方差的基本性质等理论与方法,推导出二分类敏感问题随机应答技术Warner模型在整群抽样、分层整群抽样下总体比例的估计量及其估计方差的计算公式,并在苏州大学学生婚前性行为的调查中取得了信度较高的成功应用效果。     

简单随机抽样中的“虚拟普查”技术

简单随机抽样,包括有无放回两种形式,是最基础的抽样设计.本文基于“虚拟普查”想法,对简单估计量的抽样方差给出了新的计算思路,并揭示了有无放回简单随机抽样之间的内在联系.“虚拟普查”想法的核心在于虚拟普查矩阵.该矩阵记录了逐一抽取的无放回简单随机抽样,假设拓展为普查时—–“虚拟普查”名称的由来,所有总体单元的入样轨迹.论文构建的“虚拟普查”技术框架,可以看作已有对称化论证方法和以入样指示变量为工具的论证方法的融合,对增进理解传统抽样策略,看起来具有潜在价值.作为示例,论文针对实践中常用的两个抽样策略,有放回不等概抽样和自适应整群抽样,给出了基于简单随机抽样的解读视角.     

分层三阶段及以上抽样的自加权抽样设计

大型抽样调查总是采用分层多阶段抽样.分层多阶段抽样若采用自加权的抽样设计,则总体总量的估计量形式简单,易于计算.本文提出了分层三阶段及以上抽样的自加权抽样设计方法.     

单纯随机抽样理论之我见

针对一般经济统计教材中普遍存在的关于单纯随机抽样过程中,不同抽样方法下样本方差的无偏性问题提出自己的见解.认为,抽样理论源于实践,重复抽样时有Nn个样本、不重复抽样时有CnN个样本是实际抽样调查工作中普遍采用的方式、方法,更是单纯随机抽样推断理论的源泉.在此基础上数学界将实际工作中各种可能始点的抽样方法赋予理性思考、研究,获得结论:无限制抽样和简单随机抽样条件下样本方差是总体方差的无偏估计量.     

正交表型均匀LH设计和抽样

本文提出了一种新的设计和抽样方法－正交表型均匀LH设计和抽样,证明了这种抽样空间是OALH抽样空间的优良子集。这种设计和抽样空间中所有样本都与初始设计具有同阶低偏差等一些优良性质。并将它用于数值积分,证明了对有关参数的估计的方差阶低于其他抽样。同时还给出了有关的模拟结果。     

二阶段整群抽样的样本大小估计公式及其在医疗费用调查中的应用

本文利用哥西不等式原理及求极小值方法 ,在限定抽样误差的大小使调查花费最小及限定调查花费的大小使抽样误差最小两种情况下 ,对常用的二阶段整群抽样 ,推导出其最优样本大小计算公式 ,在中国铁路职工医疗费用的调查中取得了成功的应用。     

一种满足有效样本量固定的放回抽样方法

相比不放回抽样,放回抽样的实施比较简单,操作性强,但缺点在于单元可能被重复抽到,抽出的有效样本量小于等于样本量,不是固定的。本文应用逆抽样的原理,设计了一种放回抽样方法,满足有效样本量固定,并且估计量的性质优良。     

住户调查中户内样本抽样方法的比较研究

抽样调查是获取社会经济调查数据的主要手段,其抽样设计一般采用分层多阶段不等概的抽样设计。但是,在抽样设计和实际抽样中,人们往往忽视末端样本个体的抽样,本文主要基于中国家庭动态跟踪调查数据对末端样本的概率抽样方法进行比较研究。     

多元对应分析在抽样调查分析中的应用

本文应用多元对应分析、聚类分析等数理统计方法 ,对抽样调查问卷进行分析 ,从而得出各类读者群的特征及它们之间的差异     

Tight bounds for popping algorithms

We sharpen run‐time analysis for algorithms under the partial rejection sampling framework. Our method yields improved bounds for: the cluster‐popping algorithm for approximating all‐terminal network reliability; the cycle‐popping algorithm for sampling rooted spanning trees; and the sink‐popping algorithm for sampling sink‐free orientations. In all three applications, our bounds are not only tight in order, but also optimal in30 constants.     

抽样调查的设计与分析(Ⅰ)

本文讨论抽样调查实践中有关设计和数据分析的若干问题．第一部分主要涉及设计问题．     

An Empirical Comparison of Methods for Computing Bayes Factors in Generalized Linear Mixed Models

Generalized linear mixed models (GLMM) are used in situations where a number of characteristics (covariates) affect a nonnormal response variable and the responses are correlated due to the existence of clusters or groups. For example, the responses in biological applications may be correlated due to common genetic factors or environmental factors. The clustering or grouping is addressed by introducing cluster effects to the model; the associated parameters are often treated as random effects parameters. In many applications, the magnitude of the variance components corresponding to one or more of the sets of random effects parameters are of interest, especially the point null hypothesis that one or more of the variance components is zero. A Bayesian approach to test the hypothesis is to use Bayes factors comparing the models with and without the random effects in question—this work reviews a number of approaches for estimating the Bayes factor. We perform a comparative study of the different approaches to compute Bayes factors for GLMMs by applying them to two different datasets. The first example employs a probit regression model with a single variance component to data from a natural selection study on turtles. The second example uses a disease mapping model from epidemiology, a Poisson regression model with two variance components. Bridge sampling and a recent improvement known as warp bridge sampling, importance sampling, and Chib's marginal likelihood calculation are all found to be effective. The relative advantages of the different approaches are discussed.     

Automated rejection sampling from product of distributions

Summary  Sampling from probability density functions (pdfs) has become more and more important in many areas of applied science, and has therefore been the subject of great attention. Many sampling procedures proposed allow for approximate or asymptotic sampling. On the other hand, very few methods allow for exact sampling. Direct sampling of standard pdfs is feasible, but sampling of much more complicated pdfs is often required. Rejection sampling allows to exactly sample from univariate pdfs, but has the huge drawback of needing a case-by-case calculation of a comparison function that often reveals as a tremendous chore, whose results dramatically affect the efficiency of the sampling procedure. In this paper, we restrict ourselves to a pdf that is proportional to a product of standard distributions. From there, we show that an automated selection of both the comparison function and the upper bound is possible. Moreover, this choice is performed in order to optimize the sampling efficiency among a range of potential solutions. Finally, the method is illustrated on a few examples.     

Characterization of local sampling sequences for spline subspaces

The sampling theorem is one of the most powerful tools in signal analysis. It says that to recover a function in certain function spaces, it suffices to know the values of the function on a sequence of points. Most of known results, e.g., regular and irregular sampling theorems for band-limited functions, concern global sampling. That is, to recover a function at a point or on an interval, we have to know all the samples which are usually infinitely many. On the other hand, local sampling, which invokes only finite samples to reconstruct a function on a bounded interval, is practically useful since we need only to consider a function on a bounded interval in many cases and computers can process only finite samples. In this paper, we give a characterization of local sampling sequences for spline subspaces, which is equivalent to the celebrated Schönberg-Whitney Theorem and is easy to verify. As applications, we give several local sampling theorems on spline subspaces, which generalize and improve some known results.     

Monte Carlo Integration With Acceptance-Rejection

This article considers Monte Carlo integration under rejection sampling or Metropolis-Hastings sampling. Each algorithm involves accepting or rejecting observations from proposal distributions other than a target distribution. While taking a likelihood approach, we basically treat the sampling scheme as a random design, and define a stratified estimator of the baseline measure. We establish that the likelihood estimator has no greater asymptotic variance than the crude Monte Carlo estimator under rejection sampling or independence Metropolis-Hastings sampling. We employ a subsampling technique to reduce the computational cost, and illustrate with three examples the computational effectiveness of the likelihood method under general Metropolis-Hastings sampling.     

Stability of pure strategy sampling equilibria

The idea of procedurally rational players was introduced in [4]. Among other procedures, the sampling procedure was proposed. The resulting equilibrium was called a sampling equilibrium. Evolutionary approach to the notion of a sampling equilibrium was developed in [7], where some instability results were proved. Using the concept of first-order stochastic dominance, we introduce the notion of a superior strategy and prove a stability result. The paper was supported by the Polish Government Grant No. KBN 5P03A 025 20. Author wishes to thank an anonymous referee for useful comments and suggestions.