Functional methods for time series prediction: a nonparametric approach

The problem of prediction in time series using nonparametric functional techniques is considered. An extension of the local linear method to regression with functional explanatory variable is proposed. This forecasting method is compared with the functional Nadaraya–Watson method and with finite‐dimensional nonparametric predictors for several real‐time series. Prediction intervals based on the bootstrap and conditional distribution estimation for those nonparametric methods are also compared. Copyright © 2010 John Wiley & Sons, Ltd.     

Spatial dependence model with feature difference

This paper presents a new spatial dependence model with an adjustment of feature difference. The model accounts for the spatial autocorrelation in both the outcome variables and residuals. The feature difference adjustment in the model helps to emphasize feature changes across neighboring units, while suppressing unobserved covariates that are present in the same neighborhood. The prediction at a given unit incorporates components that depend on the differences between the values of its main features and those of its neighboring units. In contrast to conventional spatial regression models, our model does not require a comprehensive list of global covariates necessary to estimate the outcome variable at the unit, as common macro-level covariates are differenced away in the regression analysis. Using the real estate market data in Hong Kong, we applied Gibbs sampling to determine the posterior distribution of each model parameter. The result of our empirical analysis confirms that the adjustment of feature difference with an inclusion of the spatial error autocorrelation produces better out-of-sample prediction performance than other conventional spatial dependence models. In addition, our empirical analysis can identify components with more significant contributions.     

A Simple Linear Regression Approach to Modeling and Forecasting Mortality Rates

Observing that a sequence of negative logarithms of 1‐year survival probabilities displays a linear relationship with the sequence of corresponding terms with a time lag of a certain number of years, we propose a simple linear regression to model and forecast mortality rates. Our model assuming the linearity between two mortality sequences with a time lag each other does not need to formulate the time trends of mortality rates across ages for mortality prediction. Moreover, the parameters of our model for a given age depend on the mortality rates for that age only. Therefore, whether the span of the study ages with the age included is widened or shortened will not affect the results of mortality fitting and forecasting for that age. In the empirical testing, the regression results using the mortality data for the UK, USA and Japan show a satisfactory goodness of fit, which convinces us of the appropriateness of the linear assumption. Empirical illustrations further show that our model's performances of fitting and forecasting mortality rates are quite satisfactory compared with the existing well‐known mortality models. Copyright © 2015 John Wiley & Sons, Ltd.     

数控机床热补偿中温度变量的选择与建模

叙述了一种在机床热补偿过程中,利用多元统计分析中的聚类分析方法对温度变量进行初步筛选,然后利用逐步回归方法获得最优模型的方法.对一台车削加工中心温度测量的变量进行了选择,并且利用逐步回归方法得到了补偿模型.结果表明,这种建模方法,不但很好地避免了温度测点的相互影响,保证了模型精度,而且节省了工作量和成本.     

基于案例学习的排球机器人运动规划及其支持向量回归实现

针对混合型控制问题,以排球任务为例研究机器人的运动规划.模拟人类球员通过经验积累而采取相应动作的行为学习模式,采取案例学习的方式解决球的初始状态微小变化(仅发球速度和角度变化)时的运动规划问题.由于支持向量回归(SVR)在处理小样本问题的优越性并受局部学习思想的启发,采用局部加权SVR(LW-SVR)实现案例学习.结果证明,LW-SVR的学习精度较RBF神经网络和SVR明显提高.     

厚板对接多道焊最高焊接温度的预测

采用多元非线性回归的方法,建立了厚板对接多道焊的最高温度与焊接线能量、层间温度、环境温度以及测点到热源中心的距离之间的数学模型.通过相关性检验、线性回归的显著性检验以及回归系数的显著性检验,证明了模型可行;同时分析了回归系数与测点到焊缝中心距离之间的关系.结果表明,它们之间的关系是一种非线性关系,而且呈现出了一定的变化趋势.     

带线性约束的最小一乘问题

本文作为[3]的续篇,考察了带线性约束的最小一乘问题的解的存在唯一性,并制定了计算方案.该计算方案利用了每一轮选代的已有结果,其效率一般比较高.     

人工智能是否促进碳解锁效率？基于技术与制度约束的门槛效应研究

随着全球对气候变化问题关注的日益增加,探究人工智能（Artificial Intelligence, AI）在促进碳解锁效率方面的潜力变得尤为重要。本研究以中国30个省市2012至2021年的面板数据为对象,构建了人工智能发展水平的评价体系和碳解锁效率的评价指标,采用熵权法和超效率SBM-GML模型分别测算了人工智能指数和碳解锁效率值,利用双固定模型和门槛回归模型分析了AI对碳解锁效率的影响以及环境规制和技术创新水平的门槛效应。结果显示,AI通过转变生产方式、优化产业结构和提升环境治理效率,显著提升了碳解锁效率;环境规制和技术创新水平对AI的影响存在显著的门槛效应,表明适度的环境规制和技术创新是促进AI实现碳解锁效率提升的关键。本研究为理解AI在促进绿色低碳转型中的作用提供了新的理论和实证支持,也为政策制定者在制定相关政策时提供了依据。     

向量多项式优化问题的数值方法

向量多项式优化问题中的目标函数和约束条件都是由多项式描述的.先将多目标多项式函数分别通过主要目标法、线性加权和法和理想点法等转化为单目标多项式函数,再利用Lasserre松弛方法求解该多项式优化问题,从而得到原向量多项式优化问题的弱有效解或有效解.数值实验结果表明该数值方法是有效的.     

基于顾客偏好的瓶装水外观感性质量评价

从感性质量评价的角度提取13个感性质量评价词对,用于全面且针对性地评价顾客对瓶装水的偏好.以"想购买"为综合感性提取瓶装水的感性质量评价要素,结合聚类分析等方法确定7种代表性样本.基于语义差异法构建瓶装水感性质量评价量表,运用多元回归模型分析建立顾客偏好与感性评价词对之间的关系模型.关系模型解决了如何识别顾客偏好和感性质量需求,以及如何将这些偏好和需求整合到产品设计过程中的问题.