回旋管及回旋潘尼管的动力学理论

本文发展了目前回旋管的动力学理论。用动力学方法,描述了回旋潘尼管的不稳定性机理,求解了偶极电流。这样,本文提出的理论,不仅可以更为精确地描写回旋管,而且可以描写新近出现的回旋潘尼管。     

巨灾风险对非寿险总产出核算影响的研究

 随着中国保险业的迅猛发展,保险核算在一国经济中变得越来越重要。越来越频繁发生的自然灾害、恐怖袭击等造成的巨大损失对联合国国民账户体系(93SNA)推荐的非寿险服务产出核算产生重大冲击,严格按照93SNA算法计算保险服务价值会导致荒谬的结果。首先本文介绍了国际上几种主要的改进方法,对每种方法的优缺点进行了深入分析。这些方法对我国改善保险核算,尤其是非寿险服务产出核算具有重要的启示意义。其次运用广东省（不含深圳）保险业的数据和期望法对06、07年的总产出进行计算,构建预测模型,并比较分析计算的结果。最后,本文提出对改进我国保险核算的建议。     

青岛一套表的实践与经验

 企业统计一套表一直是统计制度方法改革和实践中的焦点问题。本文通过对青岛市统计局企业一套表制度实施情况的案例分析,系统界定了企业一套表的概念和内涵,全面展现了企业一套表的运作过程,客观分析了制约企业一套表实施的主要原因,并提出了在现阶段实施企业统计一套表的条件和要求。     

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.     

Accelerated hazards mixture cure model

We propose a new cure model for survival data with a surviving or cure fraction. The new model is a mixture cure model where the covariate effects on the proportion of cure and the distribution of the failure time of uncured patients are separately modeled. Unlike the existing mixture cure models, the new model allows covariate effects on the failure time distribution of uncured patients to be negligible at time zero and to increase as time goes by. Such a model is particularly useful in some cancer treatments when the treat effect increases gradually from zero, and the existing models usually cannot handle this situation properly. We develop a rank based semiparametric estimation method to obtain the maximum likelihood estimates of the parameters in the model. We compare it with existing models and methods via a simulation study, and apply the model to a breast cancer data set. The numerical studies show that the new model provides a useful addition to the cure model literature.     

中国城市化与生态环境协调发展预警系统研究

本文构建了城市化与生态环境协调发展预警指标体系,并对1991～2010年中国城市化与生态环境的协调度进行了测算及预测,根据协调度建立了预警系统,并对90年代以来中国城市化与生态环境的协调发展进行了分析。结果表明,2007年以来,中国的生态环境不断恶化,城市化与生态环境的协调度不断下降,保护生态环境成为城市化进程中的重要问题。     

制造业效率区域差异及其影响因素分析——基于中国省级面板数据的研究

制造业的发展关系到第二产业乃至整个国民经济的发展,制造业的增长是中国工业经济增长的主导力量。本文运用数据包络分析法,测度了制造业各行业及中国各省制造业效率情况,然后利用中国省级制造业效率指标以及专利申请、地区经济发展水平等制造业效率影响因素的外延数据构成面板数据（Panel Data）建立模型。通过实证分析得出：地区经济的发展有利于制造业效率的提升。专利申请与制造业效率呈负相关。     

Likelihood-based and Bayesian methods for Tweedie compound Poisson linear mixed models

The Tweedie compound Poisson distribution is a subclass of the exponential dispersion family with a power variance function, in which the value of the power index lies in the interval (1,2). It is well known that the Tweedie compound Poisson density function is not analytically tractable, and numerical procedures that allow the density to be accurately and fast evaluated did not appear until fairly recently. Unsurprisingly, there has been little statistical literature devoted to full maximum likelihood inference for Tweedie compound Poisson mixed models. To date, the focus has been on estimation methods in the quasi-likelihood framework. Further, Tweedie compound Poisson mixed models involve an unknown variance function, which has a significant impact on hypothesis tests and predictive uncertainty measures. The estimation of the unknown variance function is thus of independent interest in many applications. However, quasi-likelihood-based methods are not well suited to this task. This paper presents several likelihood-based inferential methods for the Tweedie compound Poisson mixed model that enable estimation of the variance function from the data. These algorithms include the likelihood approximation method, in which both the integral over the random effects and the compound Poisson density function are evaluated numerically; and the latent variable approach, in which maximum likelihood estimation is carried out via the Monte Carlo EM algorithm, without the need for approximating the density function. In addition, we derive the corresponding Markov Chain Monte Carlo algorithm for a Bayesian formulation of the mixed model. We demonstrate the use of the various methods through a numerical example, and conduct an array of simulation studies to evaluate the statistical properties of the proposed estimators.     

基于SEM模型的流动人口迁移意愿研究

流动人口迁移意愿一直是流动人口研究的热点问题。首次将结构方程模型应用到流动人口迁移意愿影响因素的研究中,通过选取经济条件、生活环境、社会福利保障、迁移意愿4个潜在变量,采用问卷调查获取数据,构建流动人口迁移意愿SEM模型。结果表明:基于SEM的流动人口迁移意愿影响因素分析模型拟合效果较好,为流动人口服务管理相关政策的制定,提供了更加科学的依据。