Sampling design proportional to order statistic of auxiliary variable

The sampling designs dependent on sample moments of auxiliary variables are well known. Lahiri (Bull Int Stat Inst 33:133–140, 1951) considered a sampling design proportionate to a sample mean of an auxiliary variable. Sing and Srivastava (Biometrika 67(1):205–209, 1980) proposed the sampling design proportionate to a sample variance while Wywiał (J Indian Stat Assoc 37:73–87, 1999) a sampling design proportionate to a sample generalized variance of auxiliary variables. Some other sampling designs dependent on moments of an auxiliary variable were considered e.g. in Wywiał (Some contributions to multivariate methods in, survey sampling. Katowice University of Economics, Katowice, 2003a); Stat Transit 4(5):779–798, 2000) where accuracy of some sampling strategies were compared, too.These sampling designs cannot be useful in the case when there are some censored observations of the auxiliary variable. Moreover, they can be much too sensitive to outliers observations. In these cases the sampling design proportionate to the order statistic of an auxiliary variable can be more useful. That is why such an unequal probability sampling design is proposed here. Its particular cases as well as its conditional version are considered, too. The sampling scheme implementing this sampling design is proposed. The inclusion probabilities of the first and second orders were evaluated. The well known Horvitz–Thompson estimator is taken into account. A ratio estimator dependent on an order statistic is constructed. It is similar to the well known ratio estimator based on the population and sample means. Moreover, it is an unbiased estimator of the population mean when the sample is drawn according to the proposed sampling design dependent on the appropriate order statistic.     

大数据背景下基于社交网络的聚类随机游走抽样算法研究

数字经济时代,社交网络作为数字化平台经济的重要载体,受到了国内外学者的广泛关注。大数据背景下,社交网络的商业应用价值巨大,但由于其网络规模空前庞大,传统的网络分析方法 因计算成本过高而不再适用。而通过网络抽样算法获取样本网络,再推断整体网络,可节约计算资源, 因此抽样算法的好坏将直接影响社交网络分析结论的准确性。现有社交网络抽样算法存在忽略网络内部拓扑结构、容易陷入局部网络、抽样效率过低等缺陷。为了弥补现有社交网络抽样算法的缺陷,本文结合大数据社交网络的社区特征,提出了一种聚类随机游走抽样算法。该方法首先使用社区聚类算法将原始网络节点进行社区划分,得到多个社区网络,然后分别对每个社区进行随机游走抽样获取样本网 络。数值模拟和案例应用的结果均表明,聚类随机游走抽样算法克服了传统网络抽样算法的缺点,能够在降低网络规模的同时较好地保留原始网络的结构特征。此外,该抽样算法还可以并行运算,有效提升抽样效率,对于大数据背景下大规模社交网络的抽样实践具有重大现实意义。     

Nonlinear and non-gaussian state estimation: A quasi-optimal estimator

The rejection sampling filter and smoother, proposed by Tanizaki (1996, 1999), Tanizaki and Mariano (1998) and Hiirzeler and Kiinsch (1998), take a lot of time computationally. The Markov chain Monte Carlo smoother, developed by Carlin, Poison and StofFer (1992), Carter and Kohn (1994, 1996) and Geweke and Tanizaki (1999a, 1999b), does not show a good performance depending on noniinearity and nonnormality of the system in the sense of the root mean square error criterion, which reason comes from slow convergence of the Gibbs sampler. Taking into account these problems, we propose the nonlinear and non-Gaussian filter and smoother which have much less computational burden and give us relatively better state estimates, although the proposed estimator does not yield the optimal state estimates in the sense of the minimum mean square error. The proposed filter and smoother are called the quasi-optimal filter and quasi-optimal smoother in this paper. Finally, through some Monte Carlo studies, the quasi-optimal filter and smoother are compared with the rejection sampling procedure and the Markov chain Monte Carlo procedure.     

Locally optimal designs for the simple death process

We demonstrate the existence and uniqueness of optimal observation schedules for discrete observation of the simple death process. Optimality is framed in the sense of observing the process at times that maximise the Fisher information for the death rate. We examine the relationship of our designs with those obtained by other authors for the simple birth process. A number of interesting properties are uncovered. Practical considerations in the application of these designs in an experimental setting are investigated, and we examine the performance of some approximately optimal designs that are usually simpler to implement. We will show that our optimal designs are highly robust to mis-specification of the death rate.     

Population structure and correlation between auxiliary and target variables may affect precision of estimates in forest inventory

Simulations of forest inventory in several populations compared simple random with “quick probability proportional to size” (QPPS) sampling. The latter may be applied in the absence of a list sampling frame and/or prior measurement of the auxiliary variable. The correlation between the auxiliary and target variables required to render QPPS sampling more efficient than simple random sampling varied over the range 0.3–0.6 and was lower when sampling from populations that were skewed to the right. Two possible analytical estimators of the standard error of the estimate of the mean for QPPS sampling were found to be less reliable than bootstrapping.     

Estimating probabilities from invariant permutation distributions

Summary We introduce variance reduction techniques as general tools for estimating probabilities from invariant permutation distributions. The paper discusses importance sampling, antithetic sampling and control variates sampling as alternatives to uniform Monte Carlo sampling for estimating exact critical values orP-values in a broad class of permutation tests. Results may be extended to permutation confidence intervals and linear rank tests. An asymptotic theory is provided for each proposed variance reduction method. Invited paper at the Conference held in Bologna, Italy, 27–28 May 1993, on ?Statistical Tests: Methodology and Econometric Applications?.     

Scaling up a community-led health promotion initiative: Lessons learned and promising practices from the Healthy Weights for Children Project

The increase in overweight and obesity among children has emerged as an important public health issue. This trend has highlighted the need for accessible and novel approaches to support healthy weights for children and their families to prevent childhood obesity. The purpose of this article is to describe the iterative development and scale-up of a community-led, national-level project to promote healthy weights among Canadian children and families who may be experiencing vulnerabilities. In this project, the Healthy Together program was designed to engage families in an interactive program to support healthy lifestyles. The program also provides a platform for creating supportive environments for healthful lifestyles through practice and policy change. Based on a process evaluation, we describe the iterative development of Healthy Together from Phase 1 through 3 to shed light on processes shaping implementation and scale-up of the program. Lessons learned during each phase were used to refine the program and further expansion. Indicators of successful scale-up include the Healthy Together program’s cross-jurisdictional reach and promising evaluation results in real-world conditions. The practice-based program scaling approach provides practical guidance for planning and implementing similar health promotion programs in diverse communities.     

适应性区群抽样：研究流动农民工的方法与实践

 抽样难是对农民工主流群体研究稀少的原因之一,农民工具有聚集性和流动性,总体不明使得常规抽样方法抽样成本高效率低,适应性区群抽样能更经济高效地获得可进行统计推断的样本。本文以北京市城八区的农民工抽样为例,介绍了适应性区群抽样方法的基本原理、主要操作步骤、权重计算、统计推断,以及在实际操作中应注意的若干问题。     

Three optimal cut-point selection criteria based on sensitivity and specificity with user-defined weights

Methods: Based on the index S (S = SENSITIVITY (SEN) × SPECIFICITY (SPE)), the new weighted product index S_w is defined as S_w = (SEN)^2w × (SPE)^2(1-w), where (0≤w≤1). The S_w is developed to be a new tool to select the optimal cut point in ROC analysis and be compared with the other two commonly used criteria.

Results: Comparing the optimal cut point for the three criteria, the wave range of the optimal cut point for the maximized weighted Youden index criterion is the widest, the weighted closest-to-(0,1) criterion is the narrowest and the weighted product index S_w criterion lays between the ranges of the two criteria.     

Weighted Likelihood for Semiparametric Models and Two-phase Stratified Samples, with Application to Cox Regression

Abstract.  We consider semiparametric models for which solution of Horvitz–Thompson or inverse probability weighted (IPW) likelihood equations with two-phase stratified samples leads to consistent and asymptotically Gaussian estimators of both Euclidean and non-parametric parameters. For Bernoulli (independent and identically distributed) sampling, standard theory shows that the Euclidean parameter estimator is asymptotically linear in the IPW influence function. By proving weak convergence of the IPW empirical process, and borrowing results on weighted bootstrap empirical processes, we derive a parallel asymptotic expansion for finite population stratified sampling. Several of our key results have been derived already for Cox regression with stratified case–cohort and more general survey designs. This paper is intended to help interpret this previous work and to pave the way towards a general Horvitz–Thompson approach to semiparametric inference with data from complex probability samples.