进化的精神和生活的道德——鲁迅与契诃夫人生哲学比较论

面对着现实困境的鲁迅与契诃夫都珍视人生的价值,强调人的自我意识与主体力量,但是由于文化传统与历史背景的不同,他们的人生哲学各具特色。鲁迅的人生哲学体现了战斗的进化精神,契诃夫的人生哲学探讨了超脱的生活道德。     

BAYESIAN SUBSET SELECTION AND MODEL AVERAGING USING A CENTRED AND DISPERSED PRIOR FOR THE ERROR VARIANCE

This article proposes a new data‐based prior distribution for the error variance in a Gaussian linear regression model, when the model is used for Bayesian variable selection and model averaging. For a given subset of variables in the model, this prior has a mode that is an unbiased estimator of the error variance but is suitably dispersed to make it uninformative relative to the marginal likelihood. The advantage of this empirical Bayes prior for the error variance is that it is centred and dispersed sensibly and avoids the arbitrary specification of hyperparameters. The performance of the new prior is compared to that of a prior proposed previously in the literature using several simulated examples and two loss functions. For each example our paper also reports results for the model that orthogonalizes the predictor variables before performing subset selection. A real example is also investigated. The empirical results suggest that for both the simulated and real data, the performance of the estimators based on the prior proposed in our article compares favourably with that of a prior used previously in the literature.     

Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods

Summary.  A stochastic discrete time version of the susceptible–infected–recovered model for infectious diseases is developed. Disease is transmitted within and between communities when infected and susceptible individuals interact. Markov chain Monte Carlo methods are used to make inference about these unobserved populations and the unknown parameters of interest. The algorithm is designed specifically for modelling time series of reported measles cases although it can be adapted for other infectious diseases with permanent immunity. The application to observed measles incidence series motivates extensions to incorporate age structure as well as spatial epidemic coupling between communities.     

新疆兵团现代物流业发展探析

现代物流作为一种先进的组织方式和管理技术,其发展程度已成为衡量一个国家或地区现代化程度的重要标志。该文从新疆兵团物流业发展现状、面临的机遇和优势等方面对兵团工业化进程中的现代物流业进行了分析,并提出兵团发展现代物流业的对策。     

试论我国物流企业发展战略联盟的必要性

根据现代物流的发展趋势以及我国物流企业的发展现状 ,通过对几种物流企业扩张方式的比较分析 ,得出我国物流企业发展战略联盟的必要性     

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.     

论新文学思潮与新文学流派的动态关系

新文学思潮与新文学流派之间因果莫辨、源流纠缠的动态关系 ,充分证明了近百年来新文学思潮与新文学流派本身非线性、非对称的生存状态。所谓非线性与非对称 ,其实质就是不稳定性和不成熟性 ,它们直接制约着新文学近百年发展进程 ,使之在历时态和共时态上始终呈现出某种程度的不良生存状态和发展趋势。     

基于知识链的管理

论述了知识链的有关概念和特点,通过分析以往的知识链模型,提出了改进的知识链模型;对知识链管理的机制进行了分析;从控制论的角度,对知识链内部知识成长的机制进行了分析,并指出知识链内部知识转移的影响因素,为知识管理的发展提供了一定的理论依据。     

试论培训后勤职工与发挥后勤德育功能

高校后勤具有很强的德育功能。培训高校后勤职工具有多方面的重要性。高校后勤工作具有繁杂性,微利服务性和部分福利性等特点。队伍和素质参差不齐、年龄老化、学历偏低、劳动技术较弱和部分职工打工仔意识强,主人翁意识弱等现状表现出了搞好后勤职工培训的必要性和紧迫性。因此,要对后勤职工搞好四个方面的培训,通过四条途径发挥其德育功能。     

Improving Convergence of the HastingsMetropolis Algorithm with an Adaptive Proposal

The HastingsMetropolis algorithm is a general MCMC method for sampling from a density known up to a constant. Geometric convergence of this algorithm has been proved under conditions relative to the instrumental (or proposal) distribution. We present an inhomogeneous HastingsMetropolis algorithm for which the proposal density approximates the target density, as the number of iterations increases. The proposal density at the n th step is a non-parametric estimate of the density of the algorithm, and uses an increasing number of i.i.d. copies of the Markov chain. The resulting algorithm converges (in n ) geometrically faster than a HastingsMetropolis algorithm with any fixed proposal distribution. The case of a strictly positive density with compact support is presented first, then an extension to more general densities is given. We conclude by proposing a practical way of implementation for the algorithm, and illustrate it over simulated examples.