跟踪调查中的拒访行为分析——以中国家庭动态跟踪调查为例

在梳理拒访影响因素理论模型的基础上,本文运用中国家庭动态跟踪调查(China Family Panel Study,CFPS)测试初访和测试第一次追访的并行数据和问卷数据,运用Logit模型对CFPS测试第一次追访中出现的受访家户拒访行为进行分析。分析发现,CFPS追访调查样本存在选择性拒访现象。追访中,被拒访的特点和模式与发达国家的基本一致,即受到受访家户和主事者社会经济特征、参访经历等因素的影响。分析还发现,在追访中,受访对象的拒访行为是可预测的。根据前次收集的并行数据和问卷数据,可以甄别未来可能拒访的重点对象。如能依此采取特别的样本维护策略,应可降低跟踪调查样本的损失率。     

基于RAS方法的投入产出调平系统的设计与应用

RAS方法是投入产出表修正的基本工具。为节省修正时间和提高修正精度,本文基于Matlab和VB软件,编制开发了方阵和非方阵情况下的RAS调平系统。测试结果表明,在设定任意精度下,RAS智能调平系统,能够20分钟完成手动迭代600小时的操作,同时,自动完成列昂剔夫逆矩阵系数、影响力和感应度系数等各种系数的运算并输出结果。这项设计,为投入产出表的修正和相关系数的计算,带来便捷的操作环境,具有重要的应用价值。     

外商直接投资与中国雾霾污染

本文利用1998-2012年中国30个省份的空间面板数据,采用Moran‘s I和Gear's C指数对FDI与雾霾(PM2.5)污染进行了探索性空间数据分析,并基于EKC假说构建了空间面板数据模型,将经济地理嵌套权重矩阵、经济地理权重矩阵纳入静态和动态空间面板模型进行分析.实证结果发现:①相对于静态空间面板模型,动态空间计量模型能更为准确地拟合FDI对我国雾霾(PM2.5)浓度的影响过程,雾霾(PM2.5)污染表现出显著的“叠加效应”和“溢出效应”.②在经济地理嵌套权重矩阵和经济地理权重矩阵下,FDI流量每升高1％,PM25浓度分别升高0.0174％和0.0161％.FDI存量每升高1％,PM2.5浓度分别升高0.0177％和0.0163％,表明FDI是导致雾霾(PM25)浓度升高的影响因素之一,说明了我国目前吸引和利用FDI离环保目标的最优水平还有一定距离.     

Two non parametric methods for change-point detection in distribution

The change-point detection problem is determining whether a change has taken place. Two non parametric methods based on empirical likelihood and the likelihood ratio are proposed for detecting a change-point problem in distributions for independent observations. Numerical studies are carried out to evaluate the performance of the proposed methods. The simulation results demonstrate that the proposed methods are robust, that is, they perform well regardless of whether the observations are from the same distribution family.     

Optimal designs for additive mixture model with heteroscedastic errors

This paper presents a study of D- and A-optimality of direct sum designs for additive mixture models when the errors are heteroscedastic. Sufficient conditions are given so that D- and A-optimal designs for additive mixture models can be constructed from the D- and A-optimal designs for homogeneous models in sub-mixture systems.     

Composite quantile regression for varying-coefficient single-index models

ABSTRACT

The varying-coefficient single-index model (VCSIM) is a very general and flexible tool for exploring the relationship between a response variable and a set of predictors. Popular special cases include single-index models and varying-coefficient models. In order to estimate the index-coefficient and the non parametric varying-coefficients in the VCSIM, we propose a two-stage composite quantile regression estimation procedure, which integrates the local linear smoothing method and the information of quantile regressions at a number of conditional quantiles of the response variable. We establish the asymptotic properties of the proposed estimators for the index-coefficient and varying-coefficients when the error is heterogeneous. When compared with the existing mean-regression-based estimation method, our simulation results indicate that our proposed method has comparable performance for normal error and is more robust for error with outliers or heavy tail. We illustrate our methodologies with a real example.     

Affiliation networks with an increasing degree sequence

Affiliation network is one kind of two-mode social network with two different sets of nodes (namely, a set of actors and a set of social events) and edges representing the affiliation of the actors with the social events. Although a number of statistical models are proposed to analyze affiliation networks, the asymptotic behaviors of the estimator are still unknown or have not been properly explored. In this article, we study an affiliation model with the degree sequence as the exclusively natural sufficient statistic in the exponential family distributions. We establish the uniform consistency and asymptotic normality of the maximum likelihood estimator when the numbers of actors and events both go to infinity. Simulation studies and a real data example demonstrate our theoretical results.     

Further results for parallel systems with two heterogeneous exponential components

In this paper, we investigate ordering properties of lifetimes of parallel systems with two independent heterogeneous exponential components with respect to likelihood ratio and hazard rate orders. Two sufficient conditions are provided for likelihood ratio and hazard rate orders to hold between the lifetimes of two parallel systems, respectively. Moreover, we extend the results from exponential case to the proportional hazard rate models. The results established here strength some of the results known in the literature. Finally, some numerical examples are given to illustrate the theoretical results derived here as well.     

An efficient non-rejective implementation of the πps sampling designs

Poisson sampling is a method for unequal probabilities sampling with random sample size. There exist several implementations of the Poisson sampling design, with fixed sample size, which almost all are rejective methods, that is, the sample is not always accepted. Thus, the existing methods can be time-consuming or even infeasible in some situations. In this paper, a fast and non-rejective method, which is efficient even for large populations, is proposed and studied. The method is a new design for selecting a sample of fixed size with unequal inclusion probabilities. For the population of large size, the proposed design is very close to the strict πps sampling which is similar to the conditional Poisson (CP) sampling design, but the implementation of the design is much more efficient than the CP sampling. And the inclusion probabilities can be calculated recursively.     

A new confidence interval in errors-in-variables model with known error variance

This paper considers constructing a new confidence interval for the slope parameter in the structural errors-in-variables model with known error variance associated with the regressors. Existing confidence intervals are so severely affected by Gleser–Hwang effect that they are subject to have poor empirical coverage probabilities and unsatisfactory lengths. Moreover, these problems get worse with decreasing reliability ratio which also result in more frequent absence of some existing intervals. To ease these issues, this paper presents a fiducial generalized confidence interval which maintains the correct asymptotic coverage. Simulation results show that this fiducial interval is slightly conservative while often having average length comparable or shorter than the other methods. Finally, we illustrate these confidence intervals with two real data examples, and in the second example some existing intervals do not exist.