多维AR（p）模型的估计理论及应用

叙述了多维AR(p)模型的分析与处理方法、多维AR(p)预测模型的建模过程及参数估计的计算方法，并将该模型在测井曲线的预测中加以实际应用．应用结果表明：本预测模型对多维动态数据进行预测是可行的。     

基于多维统计分析方法的脑电消噪

基于二阶统计特性的主分量分析(PCA)和基于高阶统计特性的独立分量分析(ICA)是两种非常典型的多维统计分析方法.本文对PCA和ICA基本原理进行了简单介绍,并结合脑电消噪问题,对两种方法的性能和特点进行了比较.实验结果表明,在脑电消噪和特征增强等方面,独立分量分析方法具有明显的优势.     

Can cointegration‐based forecasting outperform univariate models? An application to Asian exchange rates

Conventional wisdom holds that restrictions on low‐frequency dynamics among cointegrated variables should provide more accurate short‐ to medium‐term forecasts than univariate techniques that contain no such information; even though, on standard accuracy measures, the information may not improve long‐term forecasting. But inconclusive empirical evidence is complicated by confusion about an appropriate accuracy criterion and the role of integration and cointegration in forecasting accuracy. We evaluate the short‐ and medium‐term forecasting accuracy of univariate Box–Jenkins type ARIMA techniques that imply only integration against multivariate cointegration models that contain both integration and cointegration for a system of five cointegrated Asian exchange rate time series. We use a rolling‐window technique to make multiple out of sample forecasts from one to forty steps ahead. Relative forecasting accuracy for individual exchange rates appears to be sensitive to the behaviour of the exchange rate series and the forecast horizon length. Over short horizons, ARIMA model forecasts are more accurate for series with moving‐average terms of order >1. ECMs perform better over medium‐term time horizons for series with no moving average terms. The results suggest a need to distinguish between ‘sequential’ and ‘synchronous’ forecasting ability in such comparisons. Copyright © 2002 John Wiley & Sons, Ltd.     

多维时间序列在相空间重构中的应用

介绍了多为量时间序列重构相空间技术，并以上证指数的预测为算例，具体阐述了它对原有相空间重构技术的改进，研究结果表明多变量重构相空间技术的预测效果相对于单变量重构有很大提高，文章的最后指出了在这一方法在经济预测中的应用价值和前景。     

Convergence and the Constant Dynamic Linear Model

It is well known that, as calculated using the Kalman filter recurrence relationships, the posterior parameter variance and the adaptive vector of observable constant dynamic linear models converge to limiting values. However, most proofs are tortuous, some have subtle errors and some relate only to specific cases. An elegant probabilistic convergence proof demonstrates that the limit is independent of the initial parametric prior. The result is extended to a class of multivariate dynamic linear models. Finally the proof is shown to apply to many non-observable constant DLMs. © 1997 John Wiley & Sons, Ltd.     

基于公共绿地条件的郑州市适度人口分析

综合考虑人口、资源、社会经济发展与城市绿化之间的相互关系,采用多元回归分析方法,在郑东新区建成的条件下,对郑州城市绿地系统规划中的公共绿地面积指标进行了预算.并结合国家园林城市、国家环境保护城市、生态城市等不同类型的城市建设标准,探讨了郑州市在不同城市建设类型下的城市适度人口,为郑州市中长期城市总体规划提供一定的借鉴.     

多元线性模型中随机回归系数和参数的同时线性估计的可容许性和泛容许性

多元线性模型;可容许性;泛容许性     

Modeling European industrial production with multivariate singular spectrum analysis: A cross‐industry analysis

In this paper, an optimized multivariate singular spectrum analysis (MSSA) approach is proposed to find leading indicators of cross‐industry relations between 24 monthly, seasonally unadjusted industrial production (IP) series for German, French, and UK economies. Both recurrent and vector forecasting algorithms of horizontal MSSA (HMSSA) are considered. The results from the proposed multivariate approach are compared with those obtained via the optimized univariate singular spectrum analysis (SSA) forecasting algorithm to determine the statistical significance of each outcome. The data are rigorously tested for normality, seasonal unit root hypothesis, and structural breaks. The results are presented such that users can not only identify the most appropriate model based on the aim of the analysis, but also easily identify the leading indicators for each IP variable in each country. Our findings show that, for all three countries, forecasts from the proposed MSSA algorithm outperform the optimized SSA algorithm in over 70% of cases. Accordingly, this new approach succeeds in identifying leading indicators and is a viable option for selecting the SSA choices L and r, which minimizes a loss function.     

Monthly Employment Indicators of the Euro Area and Larger Member States: Real‐Time Analysis of Indirect Estimates

The paper presents a comparative real‐time analysis of alternative indirect estimates relative to monthly euro area employment. In the experiment quarterly employment is temporally disaggregated using monthly unemployment as related series. The strategies under comparison make use of the contribution of sectoral data of the euro area and its six larger member states. The comparison is carried out among univariate temporal disaggregations of the Chow and Lin type and multivariate structural time series models of small and medium size. Specifications in logarithms are also systematically assessed. All multivariate set‐ups, up to 49 series modelled simultaneously, are estimated via the EM algorithm. Main conclusions are that mean revision errors of disaggregated estimates are overall small, a gain is obtained when the model strategy takes into account the information by both sector and member state and that larger multivariate set‐ups perform very well, with several advantages with respect to simpler models.Copyright © 2014 John Wiley & Sons, Ltd.     

Disentangling prey-handling efficiency of larval newts through multivariate prey trait analysis

Multivariate prey trait analysis is a functional approach to understand predator–prey relationships. In the present study, six prey macroinvertebrate ecological traits were used to identify several key factors in the handling efficiency of seven predatory larval newt species. The results revealed a remarkable similarity in the feeding behaviour among species, suggesting a foraging behaviour convergence among species in the prey-handling efficiency of larval newts. Nevertheless, larvae of some newt species showed clear preferences for particular categories of ecological trait; for example, Lissotriton vulgaris tended to feed on macroinvertebrate taxa with random trajectory, and Calotriton asper fed on macroinvertebrate taxa with high agility. In contrast, Mesotriton alpestris showed a clear ability to feed on prey with movable accessories (cases/tubes) and patterned concealment. This study shows how multivariate approaches can complement traditional diet analyses, and the method has wide applicability across life-stages and species.