Double-objective economic statistical design of the VP T2 control chart: Wald's identity approach

Research has shown that applying the T² control chart by using a variable parameters (VP) scheme yields rapid detection of out-of-control states. In this paper, the problem of economic statistical design of the VP T²control chart is considered as a double-objective minimization problem with the statistical objective being the adjusted average time to signal and the economic objective being expected cost per hour. We then find the Pareto-optimal designs in which the two objectives are met simultaneously by using a multi-objective genetic algorithm. Through an illustrative example, we show that relatively large benefits can be achieved by applying the VP scheme when compared with usual schemes, and in addition, the multi-objective approach provides the user with designs that are flexible and adaptive.     

Sampling within households in household surveys

Summary.  The number of people to select within selected households has significant consequences for the conduct and output of household surveys. The operational and data quality implications of this choice are carefully considered in many surveys, but the effect on statistical efficiency is not well understood. The usual approach is to select all people in each selected household, where operational and data quality concerns make this feasible. If not, one person is usually selected from each selected household. We find that this strategy is not always justified, and we develop intermediate designs between these two extremes. Current practices were developed when household survey field procedures needed to be simple and robust; however, more complex designs are now feasible owing to the increasing use of computer-assisted interviewing. We develop more flexible designs by optimizing survey cost, based on a simple cost model, subject to a required variance for an estimator of population total. The innovation lies in the fact that household sample sizes are small integers, which creates challenges in both design and estimation. The new methods are evaluated empirically by using census and health survey data, showing considerable improvement over existing methods in some cases.     

Regular graph designs

In this survey paper, measures of optimality of experimental designs are discussed from a combinatorial viewpoint. The main vehicle for this discussion is the regular graph design. This family of designs was introduced in the hope that they would be ‘good’ designs in cases where no balanced incomplete block design can exist. Various properties of these designs are discussed.     

Regression Coefficient Estimation in Dual Frame Surveys

Dual frame surveys, in which independent samples are selected from two frames to decrease survey costs or to improve coverage, can present challenges for regression coefficient estimation because of complex designs and unknown degree of overlap. In this research, we developed four regression coefficient estimators in dual frame surveys. Simulation results show that all the proposed methods work well.     

政府统计部门实施用户满意度调查的若干构想——基于欧洲统计系统的实践经验

通过用户满意度调查来测量用户对统计数据质量水平的主观感知,为统计数据质量评估与控制提供了一条重要的信息渠道。基于对政府统计部门实施用户满意度调查的社会背景及其必要性的分析,借鉴在国际同领域实践中起步较早的、较具代表性的欧洲统计系统用户满意度调查的相关实践经验,对中国政府统计部门实施用户满意度调查的制度保障、目标定位与内容设计以及调查组织实施等方面的实践要领进行了探讨。     

The pseudo‐GEE approach to the analysis of longitudinal surveys

Longitudinal surveys have emerged in recent years as an important data collection tool for population studies where the primary interest is to examine population changes over time at the individual level. Longitudinal data are often analyzed through the generalized estimating equations (GEE) approach. The vast majority of existing literature on the GEE method; however, is developed under non‐survey settings and are inappropriate for data collected through complex sampling designs. In this paper the authors develop a pseudo‐GEE approach for the analysis of survey data. They show that survey weights must and can be appropriately accounted in the GEE method under a joint randomization framework. The consistency of the resulting pseudo‐GEE estimators is established under the proposed framework. Linearization variance estimators are developed for the pseudo‐GEE estimators when the finite population sampling fractions are small or negligible, a scenario often held for large‐scale surveys. Finite sample performances of the proposed estimators are investigated through an extensive simulation study using data from the National Longitudinal Survey of Children and Youth. The results show that the pseudo‐GEE estimators and the linearization variance estimators perform well under several sampling designs and for both continuous and binary responses. The Canadian Journal of Statistics 38: 540–554; 2010 © 2010 Statistical Society of Canada     

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.     

On the planning and design of sample surveys

Surveys rely on structured questions used to map out reality, using sample observations from a population frame, into data that can be statistically analyzed. This paper focuses on the planning and design of surveys, making a distinction between individual surveys, household surveys and establishment surveys. Knowledge from cognitive science is used to provide guidelines on questionnaire design. Non-standard, but simple, statistical methods are described for analyzing survey results. The paper is based on experience gained by conducting over 150 customer satisfaction surveys in Europe, America and the Far East.     

Embedded experiments in repeated and overlapping surveys

Summary.  Statistical agencies make changes to the data collection methodology of their surveys to improve the quality of the data collected or to improve the efficiency with which they are collected. For reasons of cost it may not be possible to estimate the effect of such a change on survey estimates or response rates reliably, without conducting an experiment that is embedded in the survey which involves enumerating some respondents by using the new method and some under the existing method. Embedded experiments are often designed for repeated and overlapping surveys; however, previous methods use sample data from only one occasion. The paper focuses on estimating the effect of a methodological change on estimates in the case of repeated surveys with overlapping samples from several occasions. Efficient design of an embedded experiment that covers more than one time point is also mentioned. All inference is unbiased over an assumed measurement model, the experimental design and the complex sample design. Other benefits of the approach proposed include the following: it exploits the correlation between the samples on each occasion to improve estimates of treatment effects; treatment effects are allowed to vary over time; it is robust against incorrectly rejecting the null hypothesis of no treatment effect; it allows a wide set of alternative experimental designs. This paper applies the methodology proposed to the Australian Labour Force Survey to measure the effect of replacing pen-and-paper interviewing with computer-assisted interviewing. This application considered alternative experimental designs in terms of their statistical efficiency and their risks to maintaining a consistent series. The approach proposed is significantly more efficient than using only 1 month of sample data in estimation.     

An extension of the standardized randomized response technique to a multi-stage setup

If nonresponse and/or untruthful answering mechanisms occur, analyzing only the available cases may substantially weaken the validity of sample results. The paper starts with a reference to strategies of empirical social researchers related to respondent cooperation in surveys embedding the statistical techniques of randomized response in this framework. Further, multi-stage randomized response techniques are incorporated into the standardized randomized response technique for estimating proportions. In addition to already existing questioning designs of this family of methods, this generalization includes also several (in particular: two-stage) techniques that have not been published before. The statistical properties of this generalized design are discussed for all probability sampling designs. Further, the efficiency of the model is presented as a function of privacy protection. Hence, it can be shown that not one multi-stage design of this family at the same level of privacy protection can theoretically be more efficient than its one-stage basic version.     

Efficient estimators for adaptive stratified sequential sampling

In stratified sampling, methods for the allocation of effort among strata usually rely on some measure of within-stratum variance. If we do not have enough information about these variances, adaptive allocation can be used. In adaptive allocation designs, surveys are conducted in two phases. Information from the first phase is used to allocate the remaining units among the strata in the second phase. Brown et al. [Adaptive two-stage sequential sampling, Popul. Ecol. 50 (2008), pp. 239–245] introduced an adaptive allocation sampling design – where the final sample size was random – and an unbiased estimator. Here, we derive an unbiased variance estimator for the design, and consider a related design where the final sample size is fixed. Having a fixed final sample size can make survey-planning easier. We introduce a biased Horvitz–Thompson type estimator and a biased sample mean type estimator for the sampling designs. We conduct two simulation studies on honey producers in Kurdistan and synthetic zirconium distribution in a region on the moon. Results show that the introduced estimators are more efficient than the available estimators for both variable and fixed sample size designs, and the conventional unbiased estimator of stratified simple random sampling design. In order to evaluate efficiencies of the introduced designs and their estimator furthermore, we first review some well-known adaptive allocation designs and compare their estimator with the introduced estimators. Simulation results show that the introduced estimators are more efficient than available estimators of these well-known adaptive allocation designs.     

Simulation of close-to-reality population data for household surveys with application to EU-SILC

Statistical simulation in survey statistics is usually based on repeatedly drawing samples from population data. Furthermore, population data may be used in courses on survey statistics to explain issues regarding, e.g., sampling designs. Since the availability of real population data is in general very limited, it is necessary to generate synthetic data for such applications. The simulated data need to be as realistic as possible, while at the same time ensuring data confidentiality. This paper proposes a method for generating close-to-reality population data for complex household surveys. The procedure consists of four steps for setting up the household structure, simulating categorical variables, simulating continuous variables and splitting continuous variables into different components. It is not required to perform all four steps so that the framework is applicable to a broad class of surveys. In addition, the proposed method is evaluated in an application to the European Union Statistics on Income and Living Conditions (EU-SILC).     

The Current Duration Approach to Estimating Time to Pregnancy

Abstract. Time‐to‐pregnancy (TTP) is the duration from the time a couple starts trying to become pregnant until they succeed. It is considered one of the most direct methods to measure natural fecundity in humans. Statistical tools for designing and analysing time to pregnancy studies belong to survival analysis, but several features require special attention. Prospective designs are difficult to carry out and retrospective (pregnancy‐based) designs, being widely used in this area, do not allow efficiently including couples remaining childless. A third possible design starts from a cross‐sectional sample of couples currently trying to become pregnant, using current duration (backward recurrence time) as basis for the estimation of TTP. Regression analysis is then most conveniently carried out in the accelerated failure time model. This paper surveys some practical and technical‐statistical issues in implementing this approach in a large telephone‐based survey, the Epidemiological Observatory of Fecundity in France (Obseff).     

Randomized Response Techniques Using Maximum Likelihood Estimator

The randomized response (RR) technique with two decks of cards proposed by Odumade and Singh (2009) can always be made more efficient than the RR techniques proposed by Warner (1965), Mangat and Singh (1990), and Mangat (1994) by adjusting the proportion of cards in the decks. Arnab et al. (2012) generalized Odumade and Singh strategy (2009) for complex survey designs and wider class of estimators. In this paper improvement of Arnab et al. (2012) estimator has been made by using maximum likelihood method.     

Note on umvsu-estimation under randomized response model

Considering a class ofs randomized response trials for eliciting sensitive information from a sample survey and a class of ordered sampling designs, a uniformly minimum variance unbiased estimator of population variance (of the sensitive character) has been obtained. This note indicates that a theorem (theorem 3.9) of Cassel, Sarndal and Wretman (1977) and the results in the present note can be extended to estimation of any symmetric function of population values in the field of direct response surveys and randomized response surveys respectively.     

Optimal inference for Simon's two-stage design with over or under enrollment at the second stage

Simon's two-stage designs are widely used in clinical trials to assess the activity of a new treatment. In practice, it is often the case that the second stage sample size is different from the planned one. For this reason, the critical value for the second stage is no longer valid for statistical inference. Existing approaches for making statistical inference are either based on asymptotic methods or not optimal. We propose an approach to maximize the power of a study while maintaining the type I error rate, where the type I error rate and power are calculated exactly from binomial distributions. The critical values of the proposed approach are numerically searched by an intelligent algorithm over the complete parameter space. It is guaranteed that the proposed approach is at least as powerful as the conditional power approach which is a valid but non-optimal approach. The power gain of the proposed approach can be substantial as compared to the conditional power approach. We apply the proposed approach to a real Phase II clinical trial.     

Small area estimation with auxiliary survey data

Large governmental surveys typically provide accurate national statistics. To decrease the mean squared error of estimates for small areas, i.e., domains in which the sample size is small, auxiliary variables from administrative records are often used as covariates in a mixed linear model. It is generally assumed that the auxiliary information is available for every small area. In many cases, though, such information is available for only some of the small areas, either from another survey or from a previous administration of the same survey. The authors propose and study small area estimators that use multivariate models to combine information from several surveys. They discuss computational algorithms, and a simulation study indicates that if quantities in the different surveys are sufficiently correlated, substantial gains in efficiency can be achieved.     

我国农村住户连续性抽样调查方案设计

 在改革开放的新形势下,我国政府统计部门开展了农村住户等一系列农村统计调查,为解决“三农”问题提供了多方面的数据信息。本文通过分析总结现行农村住户抽样调查方案中存在的各种矛盾和问题,利用国际上前沿的连续性抽样调查方法作为理论基础,分别从农村住户抽样框的构建、连续各期调查样本的抽取、二维平衡轮换模式的设计、连续性抽样估计及其方差估计和连续时间序列数据的调整分析等角度提出一系列改革措施,从而设计出更加科学的调查方案,为及时、准确地搜集和提供关于“三农”问题的数据信息服务。关于其它类型的抽样调查方案亦可按照本文研究的思路类似地加以设计和解决。     

Optional versus compulsory randomized response techniques in complex surveys

In estimating the proportion of people bearing a sensitive attribute in a community, to mitigate possible evasive answer biases, Warner (J. Amer. Statist. Assoc. 60 (1965) 63) introduced a technique of randomized response (RR) in human surveys, by way of protecting individual privacy. Chaudhuri and Mukerjee (Calcutta Statist. Assoc. Bull. 34 (1985) 225; Randomized Response: Theory and Techniques, Marcel Dekker, New York) presented a modification allowing a direct response (DR) option to whom the attribute does not appear to be stigmatizing enough. Warner himself and many of his followers restrict the application of their RR devices to surveys with selection exclusively by ‘simple random sampling with replacement’. Chaudhuri (J. Statist. Plann. Inference 34 (2001a) 37; Pakistan J. Statist. 17 (3) (2001b) 259; Calcutta Statist. Assoc. Bull. 52 (205–208) (2002) 315) showed the efficacy of some of these devices when sample selection is by general unequal probabilities possibly even without replacement. Here, we present theories for unbiased estimation of the proportion along with unbiased estimation of the variances of the estimators when ‘compulsory’ or ‘optional’ RR's are gathered from persons sampled with varying probabilities. Gains in efficiency by allowing DR option rather than RR compulsion are illustrated numerically through simulation from data.     

A clinical application of expert system methodology

In recent years, several expert systems have been developed for practical applications in applied statistical methodologies. Existing expert systems in statistics have explored several areas, e.g. the determination of appropriate statistical tests, regression analysis, and determination of the ‘best’ experimental design for industrial screening experiments. We present here the DESIGN EXPERT which is a prototype expert system for the design of complex statistical experiments. It is intended for scientific investigators and statisticians who must design and analyze complex experiments, e.g. multilevel medical experiments with nested factors, repeated measures, and both fixed and random eflects. This system is ‘expert’ in the sense that it is capable of the following:(i) recognize specific types of complex experimental designs, based on the application of inference rules to non-technical information supplied by the user; (ii) encode the obtained and inferred information in a flexible general-purpose internal representation, for use by other program modules; (iii) generate analysis of variance tables for the recognized design and an appropriate BMDP runfile for data analysis, using the encoded information. DESIGN EXPERT is capable of recognizing randomized block designs, including lattice designs within embedded Latin squares, cross-over designs, split plots, nesting, repeated measures and covariates. It is written in an experimental programming language developed specifically for research in artificial intelligence.