Graphical Techniques for Selecting Explanatory Variables for Time Series Data

Bayesian model building techniques are developed for data with a strong time series structure and possibly exogenous explanatory variables that have strong explanatory and predictive power. The emphasis is on finding whether there are any explanatory variables that might be used for modelling if the data have a strong time series structure that should also be included. We use a time series model that is linear in past observations and that can capture both stochastic and deterministic trend, seasonality and serial correlation. We propose the plotting of absolute predictive error against predictive standard deviation. A series of such plots is utilized to determine which of several nested and non-nested models is optimal in terms of minimizing the dispersion of the predictive distribution and restricting predictive outliers. We apply the techniques to modelling monthly counts of fatal road crashes in Australia where economic, consumption and weather variables are available and we find that three such variables should be included in addition to the time series filter. The approach leads to graphical techniques to determine strengths of relationships between the dependent variable and covariates and to detect model inadequacy as well as determining useful numerical summaries.     

Bayesian System Reliability Prediction

In system reliability studies a common problem is the coherent assessment of system reliability, based on generic database information from components and on failure data from a system and its components in a common, but unknown, environment. A solution to this problem is given.     

Theory & Methods: Krige,Smooth, Both or Neither?

Both kriging and non-parametric regression smoothing can model a non-stationary regression function with spatially correlated errors. However comparisons have mainly been based on ordinary kriging and smoothing with uncorrelated errors. Ordinary kriging attributes smoothness of the response to spatial autocorrelation whereas non-parametric regression attributes trends to a smooth regression function. For spatial processes it is reasonable to suppose that the response is due to both trend and autocorrelation. This paper reviews methodology for non-parametric regression with autocorrelated errors which is a natural compromise between the two methods. Re-analysis of the one-dimensional stationary spatial data of Laslett (1994) and a clearly non-stationary time series demonstrates the rather surprising result that for these data, ordinary kriging outperforms more computationally intensive models including both universal kriging and correlated splines for spatial prediction. For estimating the regression function, non-parametric regression provides adaptive estimation, but the autocorrelation must be accounted for in selecting the smoothing parameter.     

西安市入境旅游市场特征及趋势预测研究

以西安市为例，运用定量分析方法，从总体特征、时间结构、空间结构、消费结构等方面对2001-2006年入境旅游市场进行分析，得出西安市入境旅游人数和旅游外汇收入在绝对值上保持较快增长，但是和全国总量以及其他旅游城市相比，市场优势地位并不明显，产业地位逐渐下降等结论。根据西安市1978-2006年的入境游客人数和旅游外汇收入数据做回归拟和，预测未来5年，西安市入境游客人数和旅游外汇收入将保持稳定增长，并提出拓展入境旅游市场的相关对策。     

社会保障制度改革的回顾与前瞻

改革开放30年来，我国在养老保险、医疗保险、失业保险、社会救助、农村社会保障制度等方面进行了深入的探索，初步建立起与市场经济体制相适应的社会保障制度，但我们还必须从社会保障的法制建设、建立多层次的社会保障体系、多渠道筹集社会保障基金、社会保障的监管等方面加以完善。     

减排约束下的中国石油需求预测

针对中国2015年和2020年CO2排放强度减排目标,建立了以CO2强度减排为主要约束,综合考虑经济增长、能源结构以及产业结构等约束的石油需求优化预测模型.预测结果显示:2015年和2020年中国石油需求量分别为5.28亿吨、6.04亿吨,该结果意味着,未来我国石油需求总量仍将有较大幅度的增加,但占一次能源比重有所下降;与其他研究结果比较发现,减排约束对石油需求的增长起到一定的抑制作用;未来中国的石油对外依存度仍将处于较高水平.     

Penalized contrast estimation in functional linear models with circular data

Our aim is to estimate the unknown slope function in the functional linear model when the response Y is real and the random function X is a second-order stationary and periodic process. We obtain our estimator by minimizing a standard (and very simple) mean-square contrast on linear finite dimensional spaces spanned by trigonometric bases. Our approach provides a penalization procedure which allows to automatically select the adequate dimension, in a non-asymptotic point of view. In fact, we can show that our penalized estimator reaches the optimal (minimax) rate of convergence in the sense of the prediction error. We complete the theoretical results by a simulation study and a real example that illustrates how the procedure works in practice.     

一致性分数阶优化灰色模型及其应用

传统的分数阶灰色预测模型在时间序列预测中具有较好的适应性和预测的有效性,但其累加和差分计算式比较复杂。一致性分数阶累加相对于一般的分数阶累加,形式更简单,更便于计算和理论推导。为了提高模型的适应性和预测能力,文章在CFGM(1,1)白化方程中引入一个新的可变系数,扩大了原有白化方程的适用范围,并在此基础上构建了一致性分数阶优化灰色模型,即CFOGM(1,1)模型。最优一致性分数阶阶数和可变系数通过PSO算法最小化平均相对误差获得。将构建的模型运用到两个实例中并与其他经典的灰色预测模型进行对比,结果表明所提出的模型具有较高的拟合和预测精度。     

陕西高校各类教育提升全人素质的预测模型

采用"高校全人素质量表"随机抽样省部属8所高校4 953名大学生,对调查数据做SPSS多元回归参数估计和AMOS结构方程模型构建。结果表明,德育教育和专业教育分别对大学生全人素质16项指数产生显著正效应;德育教育"立德效应"主提升德道品质、心身健康、人生意义等素质;专业教育"树人效应"主提升求知钻研、学术探索、技术能力、语言思维等素质。研究结果为陕西高校全人素质教育提供了实证参数和理论依据。     

TEI@I方法论及其在外汇汇率预测中的应用

基于TE I@I方法论的理论框架,构建了一个基于TE I@I方法论的外汇汇率预测模型。在此模型中,传统的经济计量模型用于处理外汇汇率的主要趋势,人工神经网络技术用于分析外汇汇率的非线性,而文本挖掘和专家系统用于处理外汇市场中的突现性和不稳定性。最后,基于集成的思想,利用支持向量回归技术对上述3个部分进行非线性集成,从而获得一个更为精确的预测结果。通过实证方法验证了基于TE I@I方法论的外汇汇率预测模型的有效性。