Reml and best linear unbiased prediction in state space models

This article takes a hierarchical model approach to the estimation of state space models with diffuse initial conditions. An initial state is said to be diffuse when it cannot be assigned a proper prior distribution. In state space models this occurs either when fixed effects are present or when modelling nonstationarity in the state transition equation. Whereas much of the literature views diffuse states as an initialization problem, we follow the approach of Sallas and Harville (1981,1988) and incorporate diffuse initial conditions via noninformative prior distributions into hierarchical linear models. We apply existing results to derive the restricted loglike-lihood and appropriate modifications to the standard Kalman filter and smoother. Our approach results in a better understanding of De Jong's (1991) contributions. This article also shows how to adjust the standard Kalman filter, the fixed inter- val smoother and the state space model forecasting recursions, together with their mean square errors, for he presence of diffuse components. Using a hierarchical model approach it is shown that the estimates obtained are Best Linear Unbiased Predictors (BLUP).     

Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics

Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important distributional deviations from Gaussianity and for flexible modelling of dependence structures. This paper develops this potential, drawing on and extending powerful results from probability theory for applications in statistical analysis. Their power is illustrated by a sustained application of OU processes within the context of finance and econometrics. We construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive OU processes, and we study these models in relation to financial data and theory.     

Smoothing a Time Series by Segments of the Data Range

We consider the problem of estimating a trend with different amounts of smoothness for segments of a time series subjected to different variability regimes. We propose using an unobserved components model to consider the existence of at least two data segments. We first fix some desired percentages of smoothness for the trend segments and deduce the corresponding smoothing parameters involved. Once the size of each segment is chosen, the smoothing formulas here derived produce trend estimates for all segments with the desired smoothness as well as their corresponding estimated variances. Empirical examples from demography and economics illustrate our proposal.     

英汉颜色词所表现的中西文化差异

由于民族风俗、地理环境、思维方式、宗教信仰、民族心理等文化背景的差异,英、汉两个民族对各种颜色词在视觉上和心理上所产生的联想、象征的意义、蕴含的寓意不尽相同,同中有异,异中有同,了解并掌握这些颜色词的语义及其引申意义差异性,对促进中西文化交流和翻译实践有一定的现实指导意义。     

中国城镇居民10年消费结构变化实证研究

通过运用扩展线性支出系统模型(ELES)对城镇居民边际消费倾向和需求收入弹性进行实证研究，得出我国城镇居民满足吃、穿为主的生存型消费需求阶段已经结束，逐步向以发展型和享受型阶段过渡．同时预测恩格尔系数会继续下降，高层次消费项目将成为新的消费增长点．最后针对我国城镇居民消费结构变化提出政策性建议．     

特定的文学作品与普遍的社会心理--从《孔雀东南飞》所表现的婆媳矛盾说起

《孔雀东南飞》是一出家庭悲剧 ,但在中国封建社会的土壤中产生的这一悲剧在全世界却具有普遍性。在老年寡妇和她的儿子、儿媳所组成的非完整的扩大家庭中 ,婆婆和儿媳争夺她们共同关心的男子的情感 ,是这个家庭内的永恒难题。家庭内的情感争夺与社会制度和民族传统并无必然联系。     

GPS/SINS组合导航系统的多传感器最优融合算法

对GPS/SINS组合导航的工作原理和模型建立进行详细的分析,设置了两个传感器。提出一种基于信息融合的导航参数最优估计滤波方法。通过计算机仿真说明该方法可提高导航系统的计算精度和速度,有较好的容错性和环境适应性,具有实际使用价值。     

Applied state space modelling of non-Gaussian time series using integration-based Kalman filtering

The main topic of the paper is on-line filtering for non-Gaussian dynamic (state space) models by approximate computation of the first two posterior moments using efficient numerical integration. Based on approximating the prior of the state vector by a normal density, we prove that the posterior moments of the state vector are related to the posterior moments of the linear predictor in a simple way. For the linear predictor Gauss-Hermite integration is carried out with automatic reparametrization based on an approximate posterior mode filter. We illustrate how further topics in applied state space modelling, such as estimating hyperparameters, computing model likelihoods and predictive residuals, are managed by integration-based Kalman-filtering. The methodology derived in the paper is applied to on-line monitoring of ecological time series and filtering for small count data.     

论循环经济发展中的生产者责任

发展循环经济,首当其冲的主体就是生产者。它不仅担任经济主体的角色,而且充当法律主体的角色。从本来意义上的生产者责任——产品质量责任,到延伸意义上的生产者责任——后产品责任,循环经济立法的这一创新,使经济法的理念和制度发生了重大的变化。该文围绕"循环经济发展中的生产者责任"这一主题,分析了这一法制创新的背景和意义,论证了该项新制度的特色,提出了设计和实施该项制度的建议。