当前我国社会信仰危机及重建之路

随着改革开放带来的社会巨大转型,我国社会出现了信仰危机,具体表现为价值迷惘、道德失范、理想幻灭以及封建迷信卷土重来和各类宗教、半宗教的乘虚而入。针对社会信仰危机产生的原因,信仰的重建必须营造良好的社会环境,并在坚持马克思主义信仰主导地位的同时,兼容其他有益的社会信仰,最终促进社会主义和谐社会的建设。     

论杂志在社会转型期的责任——新闻史上的黑幕揭发运动引发的思考

19世纪末20世纪初,一批作家和杂志记者通过“黑幕揭发”在美国发起一场旨在揭露社会腐败现象的新闻报道和文学创作运动。这场运动促进了处于转型期美国社会的全面变革,同时杂志业也迅速崛起与发展。今天的中国也在经历着社会转型,遇到了黑幕揭发作品所揭露的社会问题,黑幕揭发运动对于当代中国杂志的发展仍具有启示意义。     

The evaluation of DNA evidence in pedigrees requiring population inference

Summary. The evaluation of nuclear DNA evidence for identification purposes is performed here taking account of the uncertainty about population parameters. Graphical models are used to detail the hypotheses being debated in a trial with the aim of obtaining a directed acyclic graph. Graphs also clarify the set of evidence that contributes to population inferences and they also describe the conditional independence structure of DNA evidence. Numerical illustrations are provided by re-examining three case-studies taken from the literature. Our calculations of the weight of evidence differ from those given by the authors of case-studies in that they reveal more conservative values.     

英汉翻译中的信息转换

翻译是因社会需要而产生的一种活动 ,在社会中扮演某种角色 ,因此译者及其译本在社会中也起着重要的作用。由于每个社会群体都有自己的共有知识 ,每一种语言都有其文化特点和表达习惯 ,因此译者在其翻译过程中就要考虑用适合目的语读者的语言把原文信息重新表达出来。重新表达不仅需要词法、句法方面的转换 ,还需要采取一些补偿措施对信息进行删减、重组、异化、归化等 ,这样才能把原文的信息适当地传递给目的语读者     

A spatiotemporal auto-regressive moving average model for solar radiation

Summary.  To investigate the variability in energy output from a network of photovoltaic cells, solar radiation was recorded at 10 sites every 10 min in the Pentland Hills to the south of Edinburgh. We identify spatiotemporal auto-regressive moving average models as the most appropriate to address this problem. Although previously considered computationally prohibitive to work with, we show that by approximating using toroidal space and fitting by matching auto-correlations, calculations can be substantially reduced. We find that a first-order spatiotemporal auto-regressive (STAR(1)) process with a first-order neighbourhood structure and a Matern noise process provide an adequate fit to the data, and we demonstrate its use in simulating realizations of energy output.     

资源型城市转型主导产业的选择与培育

资源型城市转型主导产业的选择是资源型城市转型成功的关键与核心问题。本文运用定性与定量相结合的方法,提出了资源型城市转型主导产业的选择指标体系,并就如何对主导产业进行选择的方法进行了探讨。同时对资源型城市转型主导产业的培育提出了相应建议与措施。     

Estimation of dependence between paired correlated failure times in the presence of covariate measurement error

Summary. In many biomedical studies, covariates are subject to measurement error. Although it is well known that the regression coefficients estimators can be substantially biased if the measurement error is not accommodated, there has been little study of the effect of covariate measurement error on the estimation of the dependence between bivariate failure times. We show that the dependence parameter estimator in the Clayton–Oakes model can be considerably biased if the measurement error in the covariate is not accommodated. In contrast with the typical bias towards the null for marginal regression coefficients, the dependence parameter can be biased in either direction. We introduce a bias reduction technique for the bivariate survival function in copula models while assuming an additive measurement error model and replicated measurement for the covariates, and we study the large and small sample properties of the dependence parameter estimator proposed.     

Conditional Likelihood Estimators for Hidden Markov Models and Stochastic Volatility Models

ABSTRACT.  This paper develops a new contrast process for parametric inference of general hidden Markov models, when the hidden chain has a non-compact state space. This contrast is based on the conditional likelihood approach, often used for ARCH-type models. We prove the strong consistency of the conditional likelihood estimators under appropriate conditions. The method is applied to the Kalman filter (for which this contrast and the exact likelihood lead to asymptotically equivalent estimators) and to the discretely observed stochastic volatility models.     

A Sparse Implementation of the Average Information Algorithm for Factor Analytic and Reduced Rank Variance Models

Factor analytic variance models have been widely considered for the analysis of multivariate data particularly in the psychometrics area. Recently Smith, Cullis & Thompson (2001) have considered their use in the analysis of multi‐environment data arising from plant improvement programs. For these data, the size of the problem and the complexity of the variance models chosen to account for spatial heterogeneity within trials implies that standard algorithms for fitting factor analytic models can be computationally expensive. This paper presents a sparse implementation of the average information algorithm (Gilmour, Thompson & Cullis, 1995) for fitting factor analytic and reduced rank variance models.     

Improved model selection criteria for SETAR time series models

The purpose of this paper is threefold. First, we obtain the asymptotic properties of the modified model selection criteria proposed by Hurvich et al. (1990. Improved estimators of Kullback-Leibler information for autoregressive model selection in small samples. Biometrika 77, 709–719) for autoregressive models. Second, we provide some highlights on the better performance of this modified criteria. Third, we extend the modification introduced by these authors to model selection criteria commonly used in the class of self-exciting threshold autoregressive (SETAR) time series models. We show the improvements of the modified criteria in their finite sample performance. In particular, for small and medium sample size the frequency of selecting the true model improves for the consistent criteria and the root mean square error (RMSE) of prediction improves for the efficient criteria. These results are illustrated via simulation with SETAR models in which we assume that the threshold and the parameters are unknown.