西方发达国家的就业促进经验及其对中国的启示

近年来 ,随着中国改革开放的深入开展 ,城镇失业率迅速上升。西方发达国家在就业领域 ,都曾经历了失业率上升的发展阶段。为降低失业率 ,各国采取了不尽相同但却切实有效的就业促进措施 ,积累了丰富的就业促进经验。中国也可以借鉴这些就业促进经验 ,以期逐步降低失业率     

韩国人口老龄化过程及其启示

韩国人口老龄化的过程与中国有很多相似之处,经历了20世纪50年代的高生育率和死亡率降低、60年代以后的生育率下降直到目前的超低生育率,韩国人口在世纪之交快速进入老龄化阶段。预计初婚年龄的升高、持续的低生育率和育龄妇女数量的减少将会使韩国人口进一步老化。快速的人口老龄化已经为韩国社会带来了一系列的经济和社会问题,应对老龄化带来的挑战成为国家面临的重大问题。韩国的老龄化过程、现状以及采取相应的对策,将对中国具有启示意义。     

农村老年人老化态度变动轨迹:基于积极老龄化框架的实证考察

基于积极老龄化理论框架和"中国老年健康影响因素跟踪调查"面板数据,文章从积极和消极两个维度对农村老年人老化态度问题进行实证考察。研究发现,两种老化态度均以2008年为重要转折点,女性老化态度比男性更具多样性和多变性。健康、社会参与和保障都能引起老化态度的积极变动,但社会适应、子女经济支持和社区照料三个因素对消极老化态度有正向影响,而生产活动降低了积极老化态度水平。从贡献率看,健康因素对老化态度差异贡献最大,其次是保障因素、社会参与因素,健康因素更能解释积极老化态度的变化,社会参与因素对消极老化态度的贡献率更高,二者分别在女性和男性老年人群体中表现得更为明显。贡献率排在前四位的因素是精神健康、子女经济支持、医疗可及性和身体健康。用积极老龄化的观点帮助庞大的农村老年人口建立正确的老化态度将有益于养老问题的解决。     

Markov Chains and De-initializing Processes

We define a notion of de-initializing Markov chains. We prove that to analyse convergence of Markov chains to stationarity, it suffices to analyse convergence of a de-initializing chain. Applications are given to Markov chain Monte Carlo algorithms and to convergence diagnostics.     

On Non-parametric Estimation of the Survival Function with Competing Risks

The non-parametric maximum likelihood estimators (MLEs) are derived for survival functions associated with individual risks or system components in a reliability framework. Lifetimes are observed for systems that contain one or more of those components. Analogous to a competing risks model, the system is assumed to fail upon the first instance of any component failure; i.e. the system is configured in series. For any given risk or component type, the asymptotic distribution is shown to depend explicitly on the unknown survival function of the other risks, as well as the censoring distribution. Survival functions with increasing failure rate are investigated as a special case. The order restricted MLE is shown to be consistent under mild assumptions of the underlying component lifetime distributions.     

Error rates in classification consisting of discrete and continuous variables in the presence of covariates

We consider classifying an object based on mixed continuous and discrete variables between two populations. Mixed discrete and continuous covariates with identical means in both populations are amongst the variables. Under the location model with homogeneous location specific conditional dispersion matrices for both populations, the Bayes rule is given. Classification is implemented by a plug-in version of the Bayes rule with full covariate adjustment. An asymptotic expansion of the overall expected error of the procedure is derived. Our findings generalize several classical results.     

Asymptotic Analysis of the Sieve Estimator for a Class of Parabolic SPDEs

In this paper we consider the problem of estimating a coefficient of a strongly elliptic partial differential operator in stochastic parabolic equations. The coefficient is a bounded function of time. We compute the maximum likelihood estimate of the function on an approximating space (sieve) using a finite number of the spatial Fourier coefficients of the solution and establish conditions that guarantee consistency and asymptotic normality of the resulting estimate as the number of the coefficients increases. The equation is assumed diagonalizable in the sense that all the operators have a common system of eigenfunctions.     

A Bayesian discovery procedure

Summary.  We discuss a Bayesian discovery procedure for multiple-comparison problems. We show that, under a coherent decision theoretic framework, a loss function combining true positive and false positive counts leads to a decision rule that is based on a threshold of the posterior probability of the alternative. Under a semiparametric model for the data, we show that the Bayes rule can be approximated by the optimal discovery procedure, which was recently introduced by Storey. Improving the approximation leads us to a Bayesian discovery procedure, which exploits the multiple shrinkage in clusters that are implied by the assumed non-parametric model. We compare the Bayesian discovery procedure and the optimal discovery procedure estimates in a simple simulation study and in an assessment of differential gene expression based on microarray data from tumour samples. We extend the setting of the optimal discovery procedure by discussing modifications of the loss function that lead to different single-thresholding statistics. Finally, we provide an application of the previous arguments to dependent (spatial) data.     

Generalized Augmentation to Control the False Discovery Exceedance in Multiple Testing

Abstract.  A new multiple testing procedure, the generalized augmentation procedure (GAUGE), is introduced. The procedure is shown to control the false discovery exceedance and to be competitive in terms of power. It is also shown how to apply the idea of GAUGE to achieve control of other error measures. Extensions to dependence are discussed, together with a modification valid under arbitrary dependence. We present an application to an original study on prostate cancer and on a benchmark data set on colon cancer.     

A clarifying comparison of methods for controlling the false discovery rate

Traditional multiple hypothesis testing procedures fix an error rate and determine the corresponding rejection region. In 2002 Storey proposed a fixed rejection region procedure and showed numerically that it can gain more power than the fixed error rate procedure of Benjamini and Hochberg while controlling the same false discovery rate (FDR). In this paper it is proved that when the number of alternatives is small compared to the total number of hypotheses, Storey's method can be less powerful than that of Benjamini and Hochberg. Moreover, the two procedures are compared by setting them to produce the same FDR. The difference in power between Storey's procedure and that of Benjamini and Hochberg is near zero when the distance between the null and alternative distributions is large, but Benjamini and Hochberg's procedure becomes more powerful as the distance decreases. It is shown that modifying the Benjamini and Hochberg procedure to incorporate an estimate of the proportion of true null hypotheses as proposed by Black gives a procedure with superior power.