Estimating household structure in ancient China by using historical data: a latent class analysis of partially missing patterns

Summary.  Social data often contain missing information. The problem is inevitably severe when analysing historical data. Conventionally, researchers analyse complete records only. Listwise deletion not only reduces the effective sample size but also may result in biased estimation, depending on the missingness mechanism. We analyse household types by using population registers from ancient China (618–907 AD) by comparing a simple classification, a latent class model of the complete data and a latent class model of the complete and partially missing data assuming four types of ignorable and non-ignorable missingness mechanisms. The findings show that either a frequency classification or a latent class analysis using the complete records only yielded biased estimates and incorrect conclusions in the presence of partially missing data of a non-ignorable mechanism. Although simply assuming ignorable or non-ignorable missing data produced consistently similarly higher estimates of the proportion of complex households, a specification of the relationship between the latent variable and the degree of missingness by a row effect uniform association model helped to capture the missingness mechanism better and improved the model fit.     

Local influence analysis in the growth curve model with Rao's simple covariance structure

In this paper we discuss the likelihood-based local influence in a growth curve model with Rao's simple covariance structure. Under an abstract perturbation, the Hessian matrix is provided in which the eigenvector corresponding to the maximum absolute eigenvalue is used to assess the influence of observations. Specifically, we employ covariance-weighted perturbation to demonstrate the use of the proposed approach. A practical example is analysed using the proposed local influence approach.     

日本文化的发生学特点

日本文化发生于分隔、封闭的狭小地域。众多分隔、封闭的生存空间又叠加为整体结构,相互形成对立、排斥、竞争关系,从中引出你我分界的政治逻辑。这种逻辑在长期的历史发展中最终导致普遍的、恒定的价值判断的缺失。日本民族在走向"世界公民"的过程中,其特有的民族逻辑将受到世界的评判。     

国际反恐怖合作:新态势、新发展和绩效评估

9·11事件以来,在国际社会反恐斗争的强大压力下,恐怖活动开始呈现出一些新的发展趋势,如高科技化、松散化、意识形态化、本土化、年轻化等.为应对国际恐怖主义的新发展,国际社会也加强了反恐领域的合作:构建和完善国际反恐合作的法律基础,加强和提高国际反恐合作的机制与效率,提升和更新国际反恐合作的装备与技术,实施和推进国际反恐合作的综合治理战略.如何认识与评估这些反恐合作的实际成效是一个难题.通过设立合作成本、合作能力与合作收益三个评价指标体系,以上海合作组织框架下的国际反恐合作为具体案例进行深度分析,探讨评估国际反恐合作的科学路径,可以对当前的国际反恐斗争提供一些有益的参考和借鉴.     

A Multiple Imputation Approach to Linear Regression with Clustered Censored Data

Weextend Wei and Tanner's (1991) multiple imputation approach insemi-parametric linear regression for univariate censored datato clustered censored data. The main idea is to iterate the followingtwo steps: 1) using the data augmentation to impute for censoredfailure times; 2) fitting a linear model with imputed completedata, which takes into consideration of clustering among failuretimes. In particular, we propose using the generalized estimatingequations (GEE) or a linear mixed-effects model to implementthe second step. Through simulation studies our proposal comparesfavorably to the independence approach (Lee et al., 1993), whichignores the within-cluster correlation in estimating the regressioncoefficient. Our proposal is easy to implement by using existingsoftwares.     

Bayesian Analysis of Two-Regime Threshold Autoregressive Moving Average Model with Exogenous Inputs

We consider Bayesian analysis of threshold autoregressive moving average model with exogenous inputs (TARMAX). In order to obtain the desired marginal posterior distributions of all parameters including the threshold value of the two-regime TARMAX model, we use two different Markov chain Monte Carlo (MCMC) methods to apply Gibbs sampler with Metropolis-Hastings algorithm. The first one is used to obtain iterative least squares estimates of the parameters. The second one includes two MCMC stages for estimate the desired marginal posterior distributions and the parameters. Simulation experiments and a real data example show support to our approaches.     

Quasi-maximum exponential likelihood estimation for a non stationary GARCH(1,1) model

ABSTRACT

This article investigates a quasi-maximum exponential likelihood estimator(QMELE) for a non stationary generalized autoregressive conditional heteroscedastic (GARCH(1,1)) model. Asymptotic normality of this estimator is derived under a non stationary condition. A simulation study and a real example are given to evaluate the performance of QMELE for this model.