Model Averaging Multistep Prediction in an Infinite Order Autoregressive Process

The key issue in the frequentist model averaging is the choice of weights. In this paper, the authors advocate an asymptotic framework of mean-squared prediction error(MSPE) and develop a model averaging criterion for multistep prediction in an infinite order autoregressive(AR(∞)) process.Under the assumption that the order of the candidate model is bounded, this criterion is proved to be asymptotically optimal, in the sense of achieving the lowest out of sample MSPE for the samerealization pred...     

基于时变模型平均方法的我国航空客运量预测

机场扩建、政策导向、经济发展等外在因素的变化常常导致航空客运量数据发生结构性改变,其模型的设定也在很大程度上存在不确定性,因此,精准而稳定地预测航空客运量变得十分困难.为了解决以上问题,本文采用了一种时变模型平均方法(TVJMA)(Sun等,2020,2012)对全国Top 5机场的客运量进行了预测研究,该方法在模型平均时基于最小化局部Jackknife准则给出了最优的权重选择,并通过非参数估计,实现了最优权重随时间变化.实证结果表明,本文所采用的TVJMA方法显著优于其它基准模型,包括Hansen和Racine (2012)的Jackknife模型平均(JMA)以及自回归模型(AR),单整自回归移动平均模型(ARIMA),季节性单整自回归移动平均模型(SARIMA)和时变参数模型(TVP)等传统方法.进一步,对不同的预测步长,TVJMA在航空客运量的预测效果同样具有稳健性.因此,TVJMA方法可以有效地降低由于航空客运量的结构性变化和预测模型不确定性等导致的预测风险,进而做出精准而稳定的客运量预测.     

A Decision Support Model and Procedure for Selecting Interrelated R&D Projects

A decision support model with stochastic multiobjective functins and constraintsand its solution provedure are presented for selecting RD projects,in which the uncertaintyof project evaluation and selection,the interactions of technique,resource and benefit amongprojects,and the experience,knoeledge and preference of RD managers are considered .Thestatistical results of stochastic factors representing the benefit contributions and the theree kinds     

认知雷达中基于Q学习的自适应波形选择算法

自适应波形选择器是认知雷达中智能发射器的重要组成部分。有效的波形选择能够在不同的环境下选择发射最优的波形序列,从而以更高的精度追踪目标。针对雷达目标转移概率未知这一特点,把自适应波形选择问题建模为随机动态规划模型,提出应用Q学习的方法来解决这个问题。仿真结果说明,该算法接近于最优波形选择方案,并且状态估计的不确定性低于固定波形。     

信息不确定下的主动打断项目组合选择问题鲁棒优化

在项目组合选择问题中,历史数据的缺乏以及预测和估计过程中出现的不可避免的误差,会导致模型中的参数无法被准确地估计,进而给决策带来巨大的风险.因此,构建合适的鲁棒优化模型,为企业提供能有效应对参数不确定性的鲁棒解,对企业的风险防范具有极其重要的现实意义.本文首先对确定参数下的主动打断项目组合选择问题数学模型的特点进行了分析.进一步地,介绍了鲁棒优化问题中不确定情境集的概念,并给出了允许管理者根据其偏好确定不确定情境集大小的方法,构建了全新的基于情境的鲁棒优化模型,进而计算出在所规定的不确定情境集内的最坏情境下能保持可行性与最优性的鲁棒解,实现了鲁棒性与最优性间的权衡,最后,通过GAMS/BARON进行了算例分析,验证了模型的合理性与有效性.从理论上,本文首次将鲁棒优化理论扩展到了主动打断项目组合选择问题中,针对现有的项目组合选择问题鲁棒优化理论仅能应对有限个可行解的不足之处,提出了一类新的鲁棒优化方法,使其能够应对具有无穷多可行解的主动打断项目组合问题.从实践上,随着我国高新产业的发展,具有超前性与特殊性的研究与发展(RD)、信息科技与信息系统(IT/IS)等新兴项目的投资日益受到重视.相较于传统项目,这类项目的高度不确定性使得探究项目组合选择问题的鲁棒优化理论日益迫切.故而本文的研究具有明显的理论价值和现实意义.     

基于Vague集的空中目标威胁评估群决策方法

主观威胁评估方法是空中目标威胁评估的必要手段。将基于Vague集的群决策方法用于空中目标的主观威胁评估,用Vague数表示决策者对目标威胁度的主观评价值,提出一种Vague熵诱导的有序加权平均算子,用于集结各个Vague评价值,可以消除集结过程中人为误差带来的不良影响。为了直观评估各目标威胁程度大小,还提出一种基于Vague数距离和优劣点法的决策方法,来对目标威胁度进行比较和排序。算例证明该方法在空中目标威胁评估中是可行的,而且比常用的模糊综合评估方法更加有效。     

基于动态规划的企业协作研发决策模型

企业在协作研发或联合创新之前对协作企业的选择是企业开展协作研发的重要前提之一.为了保证协作研发的成功,降低研发成本的投入,防范研发不确定性带来的风险,企业应把协作研发过程分为多个阶段,不同的决策选择对研发过程和结果都会产生差异.正是基于这样的考虑建立了基本的动态规划模型来研究研发网络组织中企业研发对协作企业的选择问题.     

Option prioritization for unknown preference

An option prioritization technique is developed to efficiently elicit the preferences, both unknown and crisp, of decision makers (DMs) in strategic conflicts. In the Graph Model for Conflict Resolution, each DM has one or more options, each of which may be selected or not. A state, or possible scenario, is formed when all DMs make an option selection. The software GMCR II contains an option prioritization procedure that makes it easy for a modeller to enter a DM’s crisp preference ordering over the states using prioritized statements describing the DM’s preferred option combinations. This procedure is extended by adding two new logical connectives that describe uncertainty of preference. For each DM, a range of possible scores for each feasible state can then be calculated, facilitating the determination of a preference ordering containing uncertainty by comparing and ranking scores. To demonstrate how this new methodology can be used to represent unknown preferences in a real-world decision problem, it is applied to a Canadian dispute over proposed water exports.     

Variable Selection via Generalized SELO-Penalized Cox Regression Models

The seamless-L_0(SELO) penalty is a smooth function that very closely resembles the L_0 penalty, which has been demonstrated theoretically and practically to be effective in nonconvex penalization for variable selection. In this paper, the authors first generalize the SELO penalty to a class of penalties retaining good features of SELO, and then develop variable selection and parameter estimation in Cox models using the proposed generalized SELO(GSELO) penalized log partial likelihood(PPL) approach. The authors show that the GSELO-PPL procedure possesses the oracle property with a diverging number of predictors under certain mild, interpretable regularity conditions. The entire path of GSELO-PPL estimates can be efficiently computed through a smoothing quasi-Newton(SQN) with continuation algorithm. The authors propose a consistent modified BIC(MBIC) tuning parameter selector for GSELO-PPL, and show that under some regularity conditions, the GSELOPPL-MBIC procedure consistently identifies the true model. Simulation studies and real data analysis are conducted to evaluate the finite sample performance of the proposed method.     

跟踪机动目标的雷达波形选择新方法

针对多项式预测模型描述的机动目标,提出了一种新的雷达波形选择算法。由于机动目标的运动方程满足多项式的规律,以运动目标的多项式预测模型作为状态方程,Kalman滤波器可以很好地跟踪目标的位移和速度信息,并得到估计误差以及其预测。利用跟踪器得到目标状态估计误差的预测误差椭圆为基准,通过分数阶傅里叶变换来旋转测量误差椭圆,以使得雷达的测量误差椭圆与目标跟踪算法对目标状态的估计误差的预测的误差椭圆正交,从而得到了最优的波形选择。仿真结果表明,所提出的算法在性能上优于对比算法。     

大型钢铁企业重要备件——轧辊供应商评价

基于区间数层次分析法研究大型钢铁企业关键备件——轧辊供应商评价.轧辊供应商评价需综合考虑轧辊的性能、价格、质量、供应商的交货能力、服务水平等属性,属于多属性决策问题,而钢铁生产过程的连续性、轧辊供应商分散性、质量严格性等特点,使轧辊供应商评价更为复杂.区间数能柔性地表达决策者的偏好,适合不确定背景下的决策.以区间数层次分析法为框架研究轧辊供应商评价问题,然而,区间数判断矩阵的权重求解方法并不成熟.Mikhailov最近提出一种新方法,简单实用,不足之处是隶属度函数设置有误.文章对此加以修正.最后,将修正方法应用到国内某大型钢铁企业轧辊供应商评价之中.     

Variable Selection of Varying Dispersion Student-t Regression Models

The Student-t regression model is a useful extension of the normal model,which can be used for statistical modeling of data sets involving errors with heavy tails and/or outliers and provides robust estimation of means and regression coefficients.In this paper,the varying dispersion Student-t regression model is discussed,in which both the mean and the dispersion depend upon explanatory variables.The problem of interest is simultaneously select significant variables both in mean and dispersion model.A unified procedure which can simultaneously select significant variable is given.With appropriate selection of the tuning parameters,the consistency and the oracle property of the regularized estimators are established.Both the simulation study and two real data examples are used to illustrate the proposed methodologies.     

时滞多变量离散灰色模型及其应用

针对多变量灰色模型存在驱动因素机制不明确和缺乏驱动项引入规则的问题.通过引入时滞项控制驱动因素,构造一种新的多变量离散灰色模型,并探讨模型参数求解方法.根据灰色扩维识别方法,探索驱动项选择和时滞参数测算方法,并给出模型建模预测步骤;最后,采用该模型解决我国农村水环境与农村区域发展的滞后效应测算、建模和预测问题,实例结果表明该模型不仅精度较高,而且模型含义更明确.     

层次分析的区间估计

提出了利用数学规划和约束锥的方法 ,对层次分析法权重向量进行区间估计的理论和模型 .该模型把决策者的偏好信息视为一种约束 ,而非传统模型中的确定权重向量 .这个模型能更好地反映出决策者的偏好和决策过程中的不确定性 ,具有较好的鲁棒性 .算例结果表明 ,该模型是主观决策问题的有效的分析方法 .     

数据丢包的参数不确定下无人机滚动时域估计

针对网络中通信链路中断及系统参数不确定现象,研究了数据包丢失的参数不确定无人机系统状态估计问题,基于滚动时域估计理论和随机最小二乘理论,提出了一种分布式滚动时域估计算法。对于数据包丢失和参数不确定问题,采用已知概率的马尔可夫序列和系统矩阵扰动噪声进行建模。仿真结果表明,该算法的估计效果优于一种新的卡尔曼滤波算法。最后,研究分析了系统压缩量、数据包接收概率和时窗长度对所提算法估计性能的影响。在系统不确定性和丢包概率未知的情况下,适当增加时窗长度可以提高算法估计性能。     

阵列天线通道不一致性校正的辅加阵元法

MUSIC算法是一种基于特征值分解的超分辨DOA估计算法,在理想阵列条件下,其估计性能良好,但当信号模型与实际信号环境不匹配,即存在系统误差时,MUSIC算法的估计性能会严重下降,甚至失效。针对模型中普遍存在的通道不一致性误差,本文首先分析了此类误差对测向性能的影响,并提出了一种通道误差校正的简易算法,该方法通过对来自辅加阵元信号的数据信息进行处理,获取通道不一致性误差的估计,通过误差补偿有效地抑制了通道不一致性的影响,提高了DOA估计的性能。给出了应用该方法的具体步骤,计算机仿真结果证明了该算法的有效性。     

Variable Selection for Structural Equation with Endogeneity

This paper studies variable selection problem in structural equation of a two-stage least squares (2SLS) model in presence of endogeneity which is commonly encountered in empirical economic studies. Model uncertainty and variable selection in the structural equation is an important issue as described in Andrews and Lu (2001) and Caner (2009). The authors propose an adaptive Lasso 2SLS estimator for linear structural equation with endogeneity and show that it enjoys the oracle properties, i.e., the consistency in both estimation and model selection. In Monte Carlo simulations, the authors demonstrate that the proposed estimator has smaller bias and MSE compared with the bridge-type GMM estimator (Caner, 2009). In a case study, the authors revisit the classic returns to education problem (Angrist and Krueger, 1991) using the China Population census data. The authors find that the education level not only has strong effects on income but also shows heterogeneity in different age cohorts.     

基于非参数估计的决策融合模型及其应用研究

针对传统bayes决策融合算法中,类条件概率密度的不确定性以及固定的类条件概率密度估计值所引起的融合系统误判,本文将非参数估计方法引入到bayes决策融合算法中,提出了一种改进的bayes决策融合模型。该模型在融合过程中每次进行bayes最大后验概率准则判决后,均判断当前的类条件概率密度是否达到预期的精度,并在未达到预期精度的情况下利用parzen窗算法完成条件概率密度的逐步构造逼近。通过在丰满水电仿真系统的温度故障诊断过程中的实际应用表明,该算法在性能上优于传统bayes方法,可有效地提高诊断判决正确率。     

Robust Control of Robotic Manipulators in the Task-Space Using an Adaptive Observer Based on Chebyshev Polynomials

In this paper, an adaptive observer for robust control of robotic manipulators is proposed.The lumped uncertainty is estimated using Chebyshev polynomials. Usually, the uncertainty upper bound is required in designing observer-controller structures. However, obtaining this bound is a challenging task. To solve this problem, many uncertainty estimation techniques have been proposed in the literature based on neuro-fuzzy systems. As an alternative, in this paper, Chebyshev polynomials have been applied to uncertainty estimation due to their simpler structure and less computational load.Based on strictly-positive-real(SPR) Lyapunov theory, the stability of the closed-loop system can be verified. The Chebyshev coefficients are tuned based on the adaptation rules obtained in the stability analysis. Also, to compensate the truncation error of the Chebyshev polynomials, a continuous robust control term is designed while in previous related works, usually a discontinuous term is used. An SCARA manipulator actuated by permanent magnet DC motors is used for computer simulations.Simulation results reveal the superiority of the designed method.     

基于无模型无迹粒子滤波的编队卫星相对运动估计

由于地球引力和大气阻力等因素造成的模型不确定性,使常规滤波方法用于卫星编队飞行相对运动估计时精度不高。为克服这种影响,提出了一种融合高斯过程回归(Gaussian process regression,GPR)的无模型无迹粒子滤波(model free unscented particle filter, MF-UPF)方法。对近圆轨道的双星编队问题,新方法通过高斯过程回归对已有的量测数据学习建立相对运动模型, 有效地避免了模型不确定性造成的滤波性能下降。仿真对比验证了无模型无迹粒子滤波在编队飞行相对运动估计中的优越性。