A New Design on Attributes Single Sampling Plans

In this article, two new approaches are introduced to design attributes single plans, and the corresponding models are constructed separately. For Approach I, an algorithm is proposed to design sampling plans by setting a goal function to fulfill the two-point conditions on the operating characteristic curve. For Approach II, the plan parameters are solved by a nonlinear optimization model which minimizes the integration of the probability of acceptance in the interval from the producer's risk quality to the consumer's risk quality. Then numerical examples and discussions based on numerical computation results are given to illustrate the approaches, and tables of the designed plans under various conditions are provided. Moreover, a fact is given to be proved that there is a relation between the conventional design and the new approaches.     

基于双重抽样框的抽样估计方法研究

 随着经济社会的快速发展,抽样调查中调查对象的流动日益频繁,传统的单一抽样框很难完整覆盖流动性的目标总体,如果一定要使单一抽样框实现完整覆盖,成本必定是高昂的,甚至由于编制过程漫长使抽样调查失去其时效性。有时采用两个不完整抽样框的组合可以实现对目标总体的完整覆盖。基于双重抽样框进行抽样调查,其抽样设计工作不难,但是由于样本在两个抽样框中存在交叉,致使抽样估计甚是困难。基于此,本文将系统评述目前国外已有的各种双重抽样框估计方法,将这些方法分为分离抽样框估计和组合抽样框估计两类,并按照统一的模式比较各估计方法的功效,文章最后对我国采用双重抽样框调查进行展望。     

中美抽样调查发展的比较与思考

 中美两国引入概率抽样调查的历史背景有相似之处,但在科学应用、普及推广和应用效果方面却存在巨大差距。造成差距的主要原因是：统计体制的差异、民族文化的不同、理论与实践结合程度的差异等。文章立足中国国情,深入分析了这些原因,对构建适合中国经济社会制度和文化的政府统计调查模式进行了比较深入的思考,提出的建议有助于实现政府统计的科学发展。     

A unified framework for analysis and comparison of randomized response surveys of binary characteristics

The randomized response (RR) procedures for estimating the proportion (π)$(\pi )$ of a population belonging to a sensitive or stigmatized group ask each respondent to report a response by randomly transforming his/her true attribute into one of several response categories. In this paper, we present a common framework for discussing various RR surveys of dichotomous populations with polychotomous responses. The unified approach is focused on the substantive issues relating to respondents’ privacy and statistical efficiency and is helpful for fair comparison of various procedures. We describe a general technique for constructing unbiased estimators of π$\pi$ based on arbitrary RR procedures, from unbiased estimators based on an open survey with the same sampling design. The technique works well for any sampling design p(s)$p(s)$ and also for variance estimation. We develop an approach for comparing RR procedures, taking both respondents’ protection and statistical efficiency into account. For any given RR procedure with three or more response categories, we present a method for designing an RR procedure with a binary response variable which provides the same respondents’ protection and at least as much statistical information. This result suggests that RR surveys of dichotomous populations should use only binary response variables.     

A generalization of the logistic linear'model

Consider the logistic linear model, with some explanatory variables overlooked. Those explanatory variables may be quantitative or qualitative. In either case, the resulting true response variable is not a binomial or a beta-binomial but a sum of binomials. Hence, standard computer packages for logistic regression can be inappropriate even if an overdispersion factor is incorporated. Therefore, a discrete exponential family assumption is considered to broaden the class of sampling models. Likelihood and Bayesian analyses are discussed. Bayesian computation techniques such as Laplacian approximations and Markov chain simulations are used to compute posterior densities and moments. Approximate conditional distributions are derived and are shown to be accurate. The Markov chain simulations are performed effectively to calculate posterior moments by using the approximate conditional distributions. The methodology is applied to Keeler's hardness of winter wheat data for checking binomial assumptions and to Matsumura's Accounting exams data for detailed likelihood and Bayesian analyses.     

Bayesian quantile inference

The paper proposes a Bayesian interpretation of quantile regression that is shown to be equivalent to scale mixtures of normals leading to a skewed Laplace distribution. This representation of the model facilitates Bayesian analysis by means of Gibbs sampling with data augmentation, and nests regression in the L₁ norm as a special case. The new methods are applied to an analysis of the patents - R&D relationship for U.S. firms and unit root inference for the dollar-deutschemark exchange rate.     

抽样调查理论与方法的创新研究 ——评《辅助信息在抽样调查中的应用模型与方法研究》

 最近,中国统计出版社出版了由暨南大学刘建平教授等著的《辅助信息在抽样调查中的应用模型与方法》。本文在简要介绍该书内容的基础上,认为该书在以下两个方面进行了颇具创新性的研究：一是梳理了抽样调查的基础概念体系,为后续研究奠定了方法论基础;二是建立了辅助信息在抽样和估计方法设计中应用的模型与方法体系,从而在抽样调查各个阶段能够充分利用辅助信息,最终提高抽样调查的整体精度。它的出版,对于进一步推动我国抽样调查理论和方法研究、教学和应用水平的提高,必将发挥积极的作用。     

Problems in sampling the Native American and Alaska Native populations

Selecting a scientific national sample of Native Americans and Alaska Natives is difficult for at least four reasons: (1) they are a small proportion of the total population, (2) they are not so segregated that geographic oversampling can reach most of the population, (3) criteria for deciding who is a member of the Native American and Alaska Native population are not well defined, and (4) they are so culturally diverse that subclass estimates may be of equal or greater importance than overall estimates. The goal of this paper is to identify key design choices a statistician must face when selecting a sample of these populations, and to discuss alternative strategies. I also provide commentary on sampling plans that have been used for previous surveys, and explain why these plans, while sufficient for the purposes of their particular surveys, do not provide general solutions. Of particular importance is a careful definition of the study population. For example, some studies would focus on the reservation population, others would focus on persons listed on tribal rolls, and still others on persons eligible for coverage by the Indian Health Service. The numbers of persons included in all of these groups is far short of the two million Native Americans and Alaska Natives counted in the 1990 Census. I conclude that there is no sampling strategy that is appropriate for all surveys, but also provide suggestions for certain survey situations.     

A Sequential Markov Chain Monte Carlo Approach to Set-up Adjustment of a Process over a Set of Lots

We consider the problem of adjusting a machine that manufactures parts in batches or lots and experiences random offsets or shifts whenever a set-up operation takes place between lots. The existing procedures for adjusting set-up errors in a production process over a set of lots are based on the assumption of known process parameters. In practice, these parameters are usually unknown, especially in short-run production. Due to this lack of knowledge, adjustment procedures such as Grubbs' (1954, 1983) rules and discrete integral controllers (also called EWMA controllers) aimed at adjusting for the initial offset in each single lot, are typically used. This paper presents an approach for adjusting the initial machine offset over a set of lots when the process parameters are unknown and are iteratively estimated using Markov Chain Monte Carlo (MCMC). As each observation becomes available, a Gibbs Sampler is run to estimate the parameters of a hierarchical normal means model given the observations up to that point in time. The current lot mean estimate is then used for adjustment. If used over a series of lots, the proposed method allows one eventually to start adjusting the offset before producing the first part in each lot. The method is illustrated with application to two examples reported in the literature. It is shown how the proposed MCMC adjusting procedure can outperform existing rules based on a quadratic off-target criterion.