2005年农民增收面临的困难

20 0 4年 ,各级党委和政府认真贯彻落实“一号文件”精神 ,极大地调动起了农民农业生产积极性 ,加上市场粮价回升、农业生产气候条件适宜等多方面有利因素 ,农民增收呈现近年来少有的好形势。据统计 ,全年农民人均纯收入 2 936元 ,增加 314元 ,实际增长 6 8% ,是 1997年以来收入增加最多和增长最快的一年。但是 ,制约农民增收的深层次矛盾还没有得到有效化解 ,2 0 0 5年实现农民收入较快增长的任务还很艰巨。　　一、2 0 0 4年农民增收的特点(一 )农业收入创历史新高农业收入在农民收入中具有举足轻重的地位 ,是农民收入的最大来源。近年来…     

创新办学模式 激活办学潜力——关于福建省侨兴轻工学校教育改革的探索和实践

中职教育,由于受计划经济体制的制约和传统教育思想的束缚,长期以来,存在着重书本知识轻实践技能的弊端,是一种以教师为中心的典型模式。我校围绕“以服务为宗旨,以就业为导向,面向市场和社会办学”的指导思想,顺应形势的发展进行了积极的探索和实践,加快了中职教育教学的改革步伐,促进了学校发展。     

Factor analysis for ranked data with application to a job selection attitude survey

Summary.  Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs.     

Marginal and association regression models for longitudinal binary data with drop‐outs: A likelihood‐based approach

Longitudinal data often contain missing observations, and it is in general difficult to justify particular missing data mechanisms, whether random or not, that may be hard to distinguish. The authors describe a likelihood‐based approach to estimating both the mean response and association parameters for longitudinal binary data with drop‐outs. They specify marginal and dependence structures as regression models which link the responses to the covariates. They illustrate their approach using a data set from the Waterloo Smoking Prevention Project They also report the results of simulation studies carried out to assess the performance of their technique under various circumstances.     

儒、释、道的生育观比较研究

儒、释、道三家均有其各自的生育观及其指导下的生育实践活动.认真吸取其精华,去其糟粕,对促进两个文明建设,提高人口素质,不仅具有学术理论研究意义,也具有较强现实指导与借鉴意义.     

HEAVY-TAILED-DISTRIBUTED THRESHOLD STOCHASTIC VOLATILITY MODELS IN FINANCIAL TIME SERIES

To capture mean and variance asymmetries and time‐varying volatility in financial time series, we generalize the threshold stochastic volatility (THSV) model and incorporate a heavy‐tailed error distribution. Unlike existing stochastic volatility models, this model simultaneously accounts for uncertainty in the unobserved threshold value and in the time‐delay parameter. Self‐exciting and exogenous threshold variables are considered to investigate the impact of a number of market news variables on volatility changes. Adopting a Bayesian approach, we use Markov chain Monte Carlo methods to estimate all unknown parameters and latent variables. A simulation experiment demonstrates good estimation performance for reasonable sample sizes. In a study of two international financial market indices, we consider two variants of the generalized THSV model, with US market news as the threshold variable. Finally, we compare models using Bayesian forecasting in a value‐at‐risk (VaR) study. The results show that our proposed model can generate more accurate VaR forecasts than can standard models.     

健美操锻炼对高校女生体型的影响

通过对180名健美操选项课的女生进行为期一学年的健美操训练,探索健美操对高校女生体形和姿态的影响.实验前测验显示:多数学生的三围比例不合理,胸围、臀围较小,腹部脂肪堆积较多.通过健美操锻炼可使皮下脂肪的含量减少,去脂体重占的比例增大,三围更接近标准,肢体的柔韧性得到增强.说明健美操锻炼能够有效塑造女性的身材,使女性显得健美,更具有女性的魅力.     

Population constraints on pooled surveys in demographic hazard modeling

In non-experimental research, data on the same population process may be collected simultaneously by more than one instrument. For example, in the present application, two sample surveys and a population birth registration system all collect observations on first births by age and year, while the two surveys additionally collect information on women’s education. To make maximum use of the three data sources, the survey data are pooled and the population data introduced as constraints in a logistic regression equation. Reductions in standard errors about the age and birth-cohort parameters of the regression equation in the order of three-quarters are obtained by introducing the population data as constraints. A halving of the standard errors about the education parameters is achieved by pooling observations from the larger survey dataset with those from the smaller survey. The percentage reduction in the standard errors through imposing population constraints is independent of the total survey sample size.     

Causal inference with generalized structural mean models

Summary.  We estimate cause–effect relationships in empirical research where exposures are not completely controlled, as in observational studies or with patient non-compliance and self-selected treatment switches in randomized clinical trials. Additive and multiplicative structural mean models have proved useful for this but suffer from the classical limitations of linear and log-linear models when accommodating binary data. We propose the generalized structural mean model to overcome these limitations. This is a semiparametric two-stage model which extends the structural mean model to handle non-linear average exposure effects. The first-stage structural model describes the causal effect of received exposure by contrasting the means of observed and potential exposure-free outcomes in exposed subsets of the population. For identification of the structural parameters, a second stage 'nuisance' model is introduced. This takes the form of a classical association model for expected outcomes given observed exposure. Under the model, we derive estimating equations which yield consistent, asymptotically normal and efficient estimators of the structural effects. We examine their robustness to model misspecification and construct robust estimators in the absence of any exposure effect. The double-logistic structural mean model is developed in more detail to estimate the effect of observed exposure on the success of treatment in a randomized controlled blood pressure reduction trial with self-selected non-compliance.