引入持仓量的沪铜指数长记忆波动性研究

通过协整关系检验、误差修正模型、向量自回归模型、格兰杰因果关系检验、脉冲响应函数证明了在建立模型时引入持仓量序列的必要性。运用修正R/S分析,建立了沪铜指数收益率波动的ARFIMA、FI-GARCH、ARFIMA-FIGARCH模型,并运用此种模型对沪铜指数的收益率序列rt、收益率波动序列｜rt｜及残差序列｜εt｜进行相关研究和分析,结果表明：ARFIMA（0,d1,0）-FIGARCH（1,d2,1）模型的预测效果比较好。     

A multivariate time series model for the analysis and prediction of carbon monoxide atmospheric concentrations

We use a Bayesian multivariate time series model for the analysis of the dynamics of carbon monoxide atmospheric concentrations. The data are observed at four sites. It is assumed that the logarithm of the observed process can be represented as the sum of unobservable components: a trend, a daily periodicity, a stationary autoregressive signal and an erratic term. Bayesian analysis is performed via Gibbs sampling. In particular, we consider the problem of joint temporal prediction when data are observed at a few sites and it is not possible to fit a complex space–time model. A retrospective analysis of the trend component is also given, which is important in that it explains the evolution of the variability in the observed process.     

Bayesian inference for generalized additive mixed models based on Markov random field priors

Most regression problems in practice require flexible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal or spatial data. We present a unified approach for Bayesian inference via Markov chain Monte Carlo simulation in generalized additive and semiparametric mixed models. Different types of covariates, such as the usual covariates with fixed effects, metrical covariates with non-linear effects, unstructured random effects, trend and seasonal components in longitudinal data and spatial covariates, are all treated within the same general framework by assigning appropriate Markov random field priors with different forms and degrees of smoothness. We applied the approach in several case-studies and consulting cases, showing that the methods are also computationally feasible in problems with many covariates and large data sets. In this paper, we choose two typical applications.     

Spatial prediction and temporal backcasting for environmental fields having monotone data patterns

The authors develop a methodology for predicting unobserved values in a conditionally lognormal random spatial field like those commonly encountered in environmental risk analysis. These unobserved values are of two types. The first come from spatial locations where the field has never been monitored, the second, from currently monitored sites which have been only recently installed. Thus the monitoring data exhibit a monotone pattern, resembling a staircase whose highest step comes from the oldest monitoring sites. The authors propose a hierarchical Bayesian approach using the lognormal sampling distribution, in conjunction with a conjugate generalized Wishart distribution. This prior distribution allows different degrees of freedom to be fitted for individual steps, taking into account the differential amounts of information available from sites at the different steps in the staircase. The resulting hierarchical model is a predictive distribution for the unobserved values of the field. The method is demonstrated by application to the ambient ozone field for the southwestern region of British Columbia.     

Using intersection matrices to identify graphical structure

Intersection matrices help identify the common graphical structure of two or more objects. They arise naturally in a variety of settings. Several examples of their use in a computer algebra environment are given. These include: simplifying an expression involving array products, automating cumulant calculations, determining the behaviour of an expected value operator and identifying model hierarchy in a factorial experiment. The emphasis is placed on the graphical structure, and the symmetry of arrays help reduce the complexity of the graphical problem.     

Analysis of incomplete durations with application to contraceptive use

Models for analysing incomplete durations obtained from cross-sectional surveys are presented. The aim of the paper is to develop a framework for analysing the incomplete duration of episodes in progress at the time of the survey by formulating generalized linear models and fitting and assessing them by using standard statistical packages. The maximum quasi-likelihood method is used for model fitting. The choice of the distribution and the diagnostic procedures are discussed. Simulated data from two distributions (the Weibull and log-logistic distributions) are used to evaluate the methodology developed and to assess model misspecifications. A data set on the current use of the contraceptive pill from a cross-sectional survey in Egypt is analysed.     

俄语名词单、复数同义形式的语义功能及修辞色彩

随着现代俄语的变化和发展 ,名词的单、复数形式在某些情况下产生了同义现象 ,但各自仍有自己的语义功能及修辞色彩 ,这给正确运用名词单、复数形式带来了一定困难。本文就解决上述问题作了阐述     

一种基音提取算法

根据基音周期平滑变化的特点,基于语音信号的准周期性进行了语音信号的基音提取。该算法主要针对多带激励模型（ＭＢＥ）,采用传统的时域分析法,先用语音信号的相关性进行基音周期的估计,再由分数延迟法提取基音周期,使预测结果更加准确,更具实时性,有利于语音编码与识别。     

可持续发展社会运行机制：竞争·协同·和谐理论

本文讨论了可持续发展社会的运行机制,认为可持续发展社会是竞争、协同与和谐的统一体,规范的竞争是社会发展的不竭动力,积极的协同是社会运行内禀的制约与稳态机制,能使社会导向其更高层次的目标,和谐是使社会保持其发展活力的机制.     

Correcting the Errors: Volatility Forecast Evaluation Using High‐Frequency Data and Realized Volatilities

We develop general model‐free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy‐to‐implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability.