Bayesian Single Changepoint Estimation in a Parameter‐driven Model

In this paper, we consider the problem of estimating a single changepoint in a parameter‐driven model. The model – an extension of the Poisson regression model – accounts for serial correlation through a latent process incorporated in its mean function. Emphasis is placed on the changepoint characterization with changes in the parameters of the model. The model is fully implemented within the Bayesian framework. We develop a RJMCMC algorithm for parameter estimation and model determination. The algorithm embeds well‐devised Metropolis–Hastings procedures for estimating the missing values of the latent process through data augmentation and the changepoint. The methodology is illustrated using data on monthly counts of claimants collecting wage loss benefit for injuries in the workplace and an analysis of presidential uses of force in the USA.     

Non‐parametric Bayesian Intensity Model: Exploring Time‐to‐Event Data on Two Time Scales

Time‐to‐event data have been extensively studied in many areas. Although multiple time scales are often observed, commonly used methods are based on a single time scale. Analysing time‐to‐event data on two time scales can offer a more extensive insight into the phenomenon. We introduce a non‐parametric Bayesian intensity model to analyse two‐dimensional point process on Lexis diagrams. After a simple discretization of the two‐dimensional process, we model the intensity by a one‐dimensional piecewise constant hazard functions parametrized by the change points and corresponding hazard levels. Our prior distribution incorporates a built‐in smoothing feature in two dimensions. We implement posterior simulation using the reversible jump Metropolis–Hastings algorithm and demonstrate the applicability of the method using both simulated and empirical survival data. Our approach outperforms commonly applied models by borrowing strength in two dimensions.     

Non-stationary partition modeling of geostatistical data for malaria risk mapping

The most common assumption in geostatistical modeling of malaria is stationarity, that is spatial correlation is a function of the separation vector between locations. However, local factors (environmental or human-related activities) may influence geographical dependence in malaria transmission differently at different locations, introducing non-stationarity. Ignoring this characteristic in malaria spatial modeling may lead to inaccurate estimates of the standard errors for both the covariate effects and the predictions. In this paper, a model based on random Voronoi tessellation that takes into account non-stationarity was developed. In particular, the spatial domain was partitioned into sub-regions (tiles), a stationary spatial process was assumed within each tile and between-tile correlation was taken into account. The number and configuration of the sub-regions are treated as random parameters in the model and inference is made using reversible jump Markov chain Monte Carlo simulation. This methodology was applied to analyze malaria survey data from Mali and to produce a country-level smooth map of malaria risk.     

中小企业违约风险系统性和异质性测度——基于违约风险成分分析法的研究

与大中型企业相比,经济环境恶化或突发事件冲击使中小企业资产价值更易大幅下降,不仅单个企业违约风险急增,企业间的违约相关性也明显变大。然而不同类型中小企业违约风险变化特征仍有较大差异。为了更好测度中小企业违约风险、分析其相关性和差异性,本文在资产价值满足跳-扩散过程假定下,将或有权益分析法、组合违约风险分析与系统波动风险测度β相结合,把违约风险分解为系统成分和异质成分。系统成分越大,表明企业违约风险越易受外部经济环境和相关违约风险影响。异质成分越大则表明企业违约风险与自身异质性特征更为相关。实证研究表明,违约风险成分分析能较好解释中小企业违约风险的相关性和差异性,有助于违约风险分类管理。     

浅析跳远专项力量训练

为了更好的提高跳远专项力量训练效果,采用文献资料法、逻辑思维法和系统方法,对跳远专项力量的特点、训练方法和相应训练原则进行了初步的分析探讨。     

支撑跳跃教学中的实验研究

通过实验研究法得知 ,把心理学教学手段运用于支撑跳跃教学中 ,可使学生的恐惧心理明显减轻 ,消除学生的心理障碍 ,提高体育课教学效果     

Empirical Characteristic Function Estimation and Its Applications

This paper reviews the method of model-fitting via the empirical characteristic function. The advantage of using this procedure is that one can avoid difficulties inherent in calculating or maximizing the likelihood function. Thus it is a desirable estimation method when the maximum likelihood approach encounters difficulties but the characteristic function has a tractable expression. The basic idea of the empirical characteristic function method is to match the characteristic function derived from the model and the empirical characteristic function obtained from data. Ideas are illustrated by using the methodology to estimate a diffusion model that includes a self-exciting jump component. A Monte Carlo study shows that the finite sample performance of the proposed procedure offers an improvement over a GMM procedure. An application using over 72 years of DJIA daily returns reveals evidence of jump clustering.     

GARCH-Jump模型对跳行为捕捉能力的讨论

讨论了GARCH-Jump模型的自回归结构对跳行为的影响,阐述了该模型在两种情况下由跳引发的数据失真.分析了模型跳部件与连续路径部件之间不能等同视之原因,得出GARCH模型不能适用于处理带跳金融数据的结论.最后提出了TGARCH-Jump模型的思想修正由无条件跳带来的数据失真.     

翻译的突变论

突变论——一种横向的方法理论运用于翻译研究将给观察文本翻译过程提供一个新的角度:由连续的渐进跨语转变和非连续的突进转变交替构成。文章以涉及若干层面的"突变"译例同其渐变式译文进行了比较分析,阐明翻译活动中的这种突变现象常常是由翻译主体思维过程中的创造性突变——灵感产生的,因而译者应有努力实现必要的跨语突变的追求。     

跳深与助跑对踏跳效果影响的对比研究

通过实验并运用数理统计的相关方法,探讨了跳深和助跑对踏跳效果的影响。结果显示,跳深高度和助跑远度对踏跳效果具有一定的影响,并且两者之间具有一定的相关性。