Ridge regression is an effective tool to handle multicollinearity in regressions. It is also an essential type of shrinkage and regularization methods and is widely used in big data and distributed data applications. The divide and conquer trick, which combines the estimator in each subset with equal weight, is commonly applied in distributed data. To overcome multicollinearity and improve estimation accuracy in the presence of distributed data, we propose a Mallows-type model averaging method for ridge regressions, which combines estimators from all subsets. Our method is proved to be asymptotically optimal allowing the number of subsets and the dimension of variables to be divergent. The consistency of the resultant weight estimators tending to the theoretically optimal weights is also derived. Furthermore, the asymptotic normality of the model averaging estimator is demonstrated. Our simulation study and real data analysis show that the proposed model averaging method often performs better than commonly used model selection and model averaging methods in distributed data cases.

With the advent of modern devices, such as smartphones and wearable devices, high-dimensional data are collected on many participants for a period of time or even in perpetuity. For this type of data, dependencies between and within data batches exist because data are collected from the same individual over time. Under the framework of streamed data, individual historical data are not available due to the storage and computation burden. It is urgent to develop computationally efficient methods with statistical guarantees to analyze high-dimensional streamed data and make reliable inferences in practice. In addition, the homogeneity assumption on the model parameters may not be valid in practice over time. To address the above issues, in this paper, we develop a new renewable debiased-lasso inference method for high-dimensional streamed data allowing dependences between and within data batches to exist and model parameters to gradually change. We establish the large sample properties of the proposed estimators, including consistency and asymptotic normality. The numerical results, including simulations and real data analysis, show the superior performance of the proposed method.

Quantile regression is widely used in variable relationship research for statistical learning. Traditional quantile regression model is based on vector-valued covariates and can be efficiently estimated via traditional estimation methods. However, many modern applications involve tensor data with the intrinsic tensor structure. Traditional quantile regression can not deal with tensor regression issues well. To this end, we consider a tensor quantile regression with tensor-valued covariates and develop a novel variational Bayesian estimation approach to make estimation and prediction based on the asymmetric Laplace model and the CANDECOMP/PARAFAC decomposition of tensor coefficients. To incorporate the sparsity of tensor coefficients, we consider the multiway shrinkage priors for marginal factor vectors of tensor coefficients. The key idea of the proposed method is to efficiently combine the prior structural information of tensor and utilize the matricization of tensor decomposition to simplify the complexity of tensor coefficient estimation. The coordinate ascent algorithm is employed to optimize variational lower bound. Simulation studies and a real example show the numerical performances of the proposed method.

Finding a highly interpretable nonlinear model has been an important yet challenging problem, and related research is relatively scarce in the current literature. To tackle this issue, we propose a new algorithm called Feat-ABESS based on a framework that utilizes feature transformation and selection for re-interpreting many machine learning algorithms. The core idea behind Feat-ABESS is to parameterize interpretable feature transformation within this framework and construct an objective function based on these parameters. This approach enables us to identify a proper interpretable feature transformation from the optimization perspective. By leveraging a recently advanced optimization technique, Feat-ABESS can obtain a concise and interpretable model. Moreover, Feat-ABESS can perform nonlinear variable selection. Our extensive experiments on 205 benchmark datasets and case studies on two datasets have demonstrated that Feat-ABESS can achieve powerful prediction accuracy while maintaining a high level of interpretability. The comparison with existing nonlinear variable selection methods exhibits Feat-ABESS has a higher true positive rate and a lower false discovery rate.

新闻文本是反映国际金融市场价格波动的重要信息. 为了量化原油期货价格预测中的不确定性, 本文提出了一种新的基于新闻文本和结构化指标的多源多任务自动编码器(multi-task autoencoder, MTAE)方法, 并用于原油期货价格的预测. 首先应用Word2Vec方法提取新闻文本中的潜在特征; 针对新闻文本词向量的高维性问题, 引入MTAE方法对词向量进行降维和去噪; 其次, 利用MTAE网络拓扑结构, 对新闻文本词向量和原油期货每日涨跌信息进行融合, 以增强文本特征的可预测能力. 最后, 使用长短期记忆神经网络将文本特征与经济发展、 能源、 气候环境等原油相关指标进行集成, 预测原油期货价格. 结果表明, 本文提出的多源多任务自动编码器能够很好地提取新闻文本中的非线性特征, 具有较好的水平精度和鲁棒性.

在冷链物流企业装箱作业中, 不仅需要满足传统装箱问题中所涉及的空间约束, 同时也需要满足不同商品存放时的温度要求. 在实际作业中, 不同的商品具有不同的装箱温层区间, 温层区间不相交的商品不能被装入同一个箱子. 为维持运输中温度的稳定, 需在箱内装入一定数量的冷媒, 而这又会影响到箱内可装载的空间, 对成本产生不利影响. 为解决这一挑战, 本文研究带有温层和冷媒装载约束的冷链商品三维多箱型装箱问题, 综合考虑了冷媒、 箱子、 运输三项成本并给出其数学规划模型, 同时设计了三种用于求解该问题的启发式算法. 本文基于一大型电商物流企业的真实订单数据, 改造生成了不同规模的测试算例以检验各算法的表现. 数值实验结果表明, 所提出的三种算法都能在合理的时间内给出该问题的较优解. 通过与各算例最优解的下界比较, 发现基于物品分组的启发式算法求得的平均成本距下界最近, 但求解速度较慢. 求解速度最快的为结合模拟退火的基于极端点法的构造型启发式算法, 但其在大规模测试算例上的表现要差于其他两种算法. 通过进一步对比分析三种算法的适用场景, 能够为企业提供管理见解与启示, 有望改善冷链物流企业的实际装箱操作流程.

We consider the problem of multi-task regression with time-varying low-rank patterns, where the collected data may be contaminated by heavy-tailed distributions and/or outliers. Our approach is based on a piecewise robust multi-task learning formulation, in which a robust loss function—not necessarily to be convex, but with a bounded derivative—is used, and each piecewise low-rank pattern is induced by a nuclear norm regularization term. We propose using the composite gradient descent algorithm to obtain stationary points within a data segment and employing the dynamic programming algorithm to determine the optimal segmentation. The theoretical properties of the detected number and time points of pattern shifts are studied under mild conditions. Numerical results confirm the effectiveness of our method.

在无人机电力巡检过程中, 一个待巡检部件通常对应多个位置不同且均符合拍摄要求的任务点, 这些拍摄任务点构成一个集合任务. 为了保证巡检质量, 通常要求无人机多次采集待巡检部件的图片, 即访问集合任务中的多个拍摄任务点. 本文针对上述特点, 将面向集合任务的多无人机电力巡检任务分配问题建模为最小最大化多站点家庭旅行商问题(minmax multi-depot family traveling salesman problem, Minmax-MDFTSP), 并设计了一种强化遗传算法框架. 在该框架下, 提出了染色体校验及修正机制、 组合交换变异算子、 基于贪婪策略的局部调优算子, 设计了基于强化学习的遗传算法参数调优方法. 性能实验结果表明, 本文方法在求解质量和求解效率方面均具有明显优势. 此外, 通过消融实验验证了强化遗传算法框架中各个部分的有效性. 最后结合实际场景下的具体案例, 通过实地飞行验证了本文方法相对于现有巡检方式的优势.

为解决可编组自动驾驶公交和传统电动公交混合运营下的行车计划问题, 本文考虑二者的功能和成本差异, 结合跨线调度和分段充电策略构建混合公交系统行车计划优化模型. 模型以两类公交的运行成本、 空驶成本、 购置成本以及充电成本最小化为目标, 决策场站车队规模及车辆的车次链. 针对该整数非线性规划模型, 本文设计"初始解生成与列生成框架"的两阶段求解算法, 并选取北京市某场站四条运营线路进行案例分析. 结果表明: 与单线调度的公交系统相比, 跨线调度策略能够提高7.2%的车辆利用率, 降低23.3%的运行成本和21.93%的购置成本. 相比于多车型的传统电动公交系统, 可编组自动驾驶公交的引入使得编组单元的平均使用次数达到3.58次, 系统的平均车辆使用次数增加0.53次, 有效提高车辆使用率. 为混合公交系统下多线路公交行车计划提供优化建议.

Single-index model offers the greater flexibility of modelling than generalized linear models and also retains the interpretability of the model to some extent. Although many standard approaches such as kernels or penalized/smooothing splines were proposed to estimate smooth link function, they cannot approximate complicated unknown link functions together with the corresponding derivatives effectively due to their poor approximation ability for a finite sample size. To alleviate this problem, this paper proposes a semiparametric least squares estimation approach for a single-index model using the rectifier quadratic unit (ReQU) activated deep neural networks, called deep semiparametric least squares (DSLS) estimation method. Under some regularity conditions, we show non-asymptotic properties of the proposed DSLS estimator, and evidence that the index coefficient estimator can achieve the semiparametric efficiency. In particular, we obtain the consistency and the convergence rate of the proposed DSLS estimator when response variable is conditionally sub-exponential. This is an attempt to incorporate deep learning technique into semiparametrically efficient estimation in a single index model. Several simulation studies and a real example data analysis are conducted to illustrate the proposed DSLS estimator.

Bilal分布作为寿命模型, 在生存分析中起着重要作用.文章分别在简单随机抽样(SRS)和排序集抽样(RSS)下研究了Bilal分布中参数$\theta$的修正极大似然估计(MLE),获得了该修正MLE的有限样本性质和大样本性质. 数值结果表明,同等样本容量下的RSS修正MLE比SRS修正MLE有效.数值模拟也显示同种抽样下MLE 的均方误差和修正MLE的方差是非常接近的,但具有显性解的修正MLE在实际中使用更方便.

参考质量效应下, 农产品的品牌商誉、 质量以及市场需求之间存在复杂的动态反馈关系, 使得生产、 保鲜及品牌营销策略的绩效具有非线性和反直观性. 本文构建考虑消费者参考质量效应的区域品牌农产品三级供应链系统动力学模型, 运用Vensim DSS软件的内置Powell爬山算法, 生成不同情形下生产商产出质量努力、 经销商保鲜努力及零售商品牌营销努力水平的最优组合, 并基于此设计4种仿真情景, 模拟不同努力水平组合下区域品牌商誉、 各主体利润的动态演化过程, 分析质量、 保鲜和营销策略的动态影响机制, 以优化供应链的运营策略. 结果表明: 生产商的质量努力水平是控制系统绩效演化动态的关键决策变量, 农产品的品牌商誉及经销商、 零售商利润与产出质量高度正相关, 但保持高产出质量的努力成本投入会使生产商自身利润受损严重; 在参考质量效应影响下, 品牌商誉由非均衡动态趋向均衡状态的演化路径充满振荡, 且波谷随生产商质量努力水平的降低而下降明显. 基于系统动力学建模与仿真, 从行为运营的视角研究供应链主体决策的绩效动态, 是对当前基于分析模型方法、 聚焦均衡控制策略研究的有益补充.

区间值数据比点值数据包含更多的信息,已成为复杂大数据背景下应用研究的热点.现有的区间数回归模型大多基于代表元素框架或者传统区间数减法构建,但代表元素框架模型未把区间整体参加运算易丢失区间信息,传统区间数减法存在区间数运算结果不合理现象. 为解决上述问题,文章提出区间数广义Hukuhara(gH)差的框架下$p$维区间数线性回归模型,在保证区间运算结果合理的基础上, 基于支撑函数构建回归残差评估方法,推导了模型的最小二乘估计,并讨论了一维情况下回归参数的形态、特点、性质.最后利用蒙特卡罗模拟验证了新方法的有效性和精度.

考虑定制出行背景下常规乘客和特殊乘客的异质性需求, 从网约共享出行平台运营视角分析了乘客出行选择和司机供给选择, 对定制-常规双服务模式进行建模求解, 推导需求响应的双边定价策略. 研究表明, 为实现利润最大化, 平台应分别在小规模、 中等规模和大规模市场中选择不提供服务、 提供定制-常规双服务和仅提供定制服务. 在双服务模式下, 常规乘客有效到达率随市场规模呈倒U型变化, 平台通过对不同乘客实施价格歧视, 可显著提高市场覆盖率和盈利能力. 尽管双服务模式提升了整体社会福利, 却可能降低常规乘客的消费者剩余, 且在市场规模较大或司机提供定制服务成本较低时更为明显. 本研究为网约共享出行平台运营定制服务提供了理论和实践启示.

Doubly truncated data arise when the survival times of interest are observed only if they fall within certain random intervals. In this paper, we consider a semiparametric additive hazards model with doubly truncated data, and propose a weighted estimating equation approach to estimate the regression coefficients, where the weights are estimated both parametrically and nonparametrically. The asymptotic properties of the resulting estimators are established. Simulation studies demonstrate that the proposed estimators perform well in a finite sample. An application to Parkinson's disease data is provided.

区间删失数据和面板计数数据是生存分析中常见的两类不完整数据.文章考虑两类数据的联合建模分析,同时纳入了带有信息的观测过程.引入两个脆弱项来刻画失效时间、计数过程和观测过程的相关性,并对三者进行联合建模研究,利用两步估计法实现所建模型的参数估计.然后进行数值模拟,模拟结果表明所提出的方法表现良好.最后将所提出的方法应用于同种异体心脏移植血管病变研究的真实数据.

基于文献分析构建了以承发包双方之间竞合关系为自变量、 项目价值增值为因变量、 项目不确定性为调节变量的理论模型. 通过规范性文件分析和专家访谈, 开发竞合关系测量量表, 并结合探索性和验证性因子分析法对量表进行修订和验证; 基于194份有效数据, 采用层级回归分析法对理论模型进行实证检验. 研究表明: 1) 竞合关系具有二阶维度结构内涵, 其中, 合作关系由承包人介入时点、 承包人合理化建议的提出、 信息沟通3个子维度构成, 竞争关系由合同计价方式、 "优质优价"条款、 承包人合理化建议的利益分享、 监管力度、 争议解决5个子维度构成; 2) EPC (engineering-procurement-construction)项目中竞合关系与价值增值之间呈倒 U 型关系; 3) 项目外部环境不确定性和参与者行为不确定性负向调节倒U型关系, 项目自身不确定性正向调节倒U型关系.

文章研究在响应变量缺失下高维半参数变系数测量误差模型的参数估计与变量选择问题.首先, 基于逆概率加权方法分别构造了纠偏参数部分和非参数部分的估计,在适当的条件下证明纠偏的非参数估计具有渐近正态性. 然后,构造了纠偏参数部分的经验对数似然比统计量, 同时建议惩罚经验似然(PEL)进行变量选择, 在适当的条件下证明了所提出的惩罚经验估计具有Oracle特征且在零假设下服从渐近卡方分布. Monte Carlo模拟研究表明,建议的估计在有限样本表现较好, 最后给出一个实例研究.

流动资金是企业赖以生存和发展的根本, 经济不确定性、 不确定需求、 低效率管理等易造成资金流管理混乱. 企业还会受到生产成本、 订购成本、 融资利率等不确定性的干扰, 这将直接影响他们的运营策略, 进而造成系统出现严重的波动性. 协同控制的优势在于整体协同合作, 使系统更具柔性, 可高效地应对变化和挑战. 对此, 加强资金协同管理不仅能提高其利用价值和效率, 还能提高系统的稳定性. 为此, 设计一种动态环境下的资金协同控制策略以实现系统的降本增效. 首先, 考虑一个二级供应链金融系统, 构建一个包含节点企业的状态资金转移和系统总成本方程的基础模型. 然后, 利用T-S模糊系统将基础模型转化模糊多模型系统, 来降低协同控制切换过程中对系统造成的干扰. 其次, 通过多个仿真实验来验证方法的可行性和控制策略的有效性. 最后, 通过仿真结果分析给出了一些管理启示和决策支持.

To learn the subgroup structure generated by multidimensional interaction, we propose a novel multiview subgroup integration technique based on tensor decomposition. Compared to the traditional subgroup analysis that can only handle single-view heterogeneity, our proposed method achieves a greater level of homogeneity within the subgroups, leading to enhanced interpretability and predictive power. For computational readiness of the proposed method, we build an algorithm that incorporates pairwise shrinkage-encouraging penalties and ADMM techniques. Theoretically, we establish the asymptotic consistency and normality of the proposed estimators. Extensive simulation studies and real data analysis demonstrate that our proposal outperforms other methods in terms of prediction accuracy and grouping consistency. In addition, the analysis based on the proposed method indicates that intergenerational care significantly increases the risk of chronic diseases associated with diet and fatigue in all provinces while only reducing the risk of emotion-related chronic diseases in the eastern coastal and central regions of China.

支持向量回归(SVR)模型的耐噪性问题一直是当前回归领域的流行研究方向之一.文章构建了一种稳健的损失函数(即G-Loss),并提出了一种基于核回归的在线SVR 算法(OGSVR) 以处理含噪数据. G-Loss通过分区域设置惩罚权重, 同时引入$\varepsilon$不敏感带解决模型的稀疏性问题. 为解决动态数据流回归问题,文章构建了在线SVR 算法, 并采用随机梯度下降算法进行求解.在合成数据集, UCI 基准数据集和真实数据集上的多项实验表明, OGSVR算法与在线$\varepsilon$-SVR、 在线LS-SVR 和在线Canal-SVR算法相比在不同噪声水平的数据集上获得了良好的表现, 在多数情况下,可以在较短的运行时间内获得较高的预测精度.

企业生态系统通过网络结构与功能机制, 在生产要素的运作过程中考虑上下游关系和空间布局, 形成关联网络. 为探究企业生态系统的作用机制, 分析在不同场景下适应性互惠的主体如何评价, 本文构建了整合式评价方法. 通过节点网络的结构特征, 基于系统群聚特征(如功能性或资源性群聚), 判断链群的状态, 考虑企业生态系统网络结构, 通过链群互惠判断和链群互惠效用的联合分布来改进链群互惠效用. 同时, 通过网络节点互惠判断区分连接形式(与现有互惠节点连接、 与新节点连接和随机连接), 从而量化网络节点在生态系统中的适应性变化. 结合高阶网络结构参数的共圈特征, 修正节点的生态系统适应度. 本研究应用场景涉及评价要素的整合方式, 以及整合哪些要素的判据, 同时解析了特定场景下的嵌入式评价和脱嵌式评价模式. 其中嵌入式评价侧重于链群评价, 而脱嵌式评价侧重于节点评价. 经过调节后, 企业生态系统存在多种状态, 如理想状态、 正向演化状态、 结构调整状态、 系统呈扩张状态和不适配状态等. 本研究为企业生态系统研究提供了整合式评价的研究视角, 并在不同应用场景下分析了适应性互惠的决策建议, 有助于企业基于适应性互惠进行生态系统结构的优化和调节.

Combining p-values is a well-known issue in statistical inference. When faced with a study involving $m$ p-values, determining how to effectively combine them to arrive at a comprehensive and reliable conclusion becomes a significant concern in various fields, including genetics, genomics, and economics, among others. The literature offers a range of combination strategies tailored to different research objectives and data characteristics. In this work, we aim to provide users with a systematic exploration of the p-value combination problem. We present theoretical results for combining p-values using a logarithmic transformation, which highlights the benefits of this approach. Additionally, we propose a combination strategy together with its statistical properties utilizing the gold section method, showcasing its performance through extensive computer simulations. To further illustrate its effectiveness, we apply this approach to a real-world scenario.

异常检测问题是一个长期受到关注的话题,具有广泛的应用价值和研究意义.处理异常检测问题的机器学习算法有很多,但它们对于检测结果的错误发现的程度没有明确的统计学意义上的保证.文章以在线多重假设检验问题的角度去研究,提出了可控制在线错误发现率且不依赖模型和分布假设的基于共形推断的异常检测方法,该方法可以内嵌不同的异常检测的机器学习算法和在线多重假设检验算法,是实现异常检测的一般化方法框架.最后通过模拟数据实验验证文章方法的有效性,并将其应用于服务器数据集,实现异常点检测.

考虑目标函数和约束函数都是上半连续拟凸函数的拟凸优化问题,引入较文献(Suzuki, 2021)弱的约束规范条件,刻画了该问题基于Greenberg-Pierskalla次微分的KKT类最优性条件,改进了前人的相关结论.

多视角和多层次是粒计算(granular computing)中问题求解的两个基本原则. 划分序乘积空间作为一种新的粒计算模型, 遵循多视角和多层次的原则, 能够从多个视角和多个层次来描述和解决问题. 但是划分序乘积空间是一种格结构, 在划分序乘积空间中寻找合适的问题求解层通常是一个NP难问题, 特别是当视角和层次存在冗余时, 会导致划分序乘积空间结构庞大且复杂. 因此通过对视角和层次的约简, 可以有效降低划分序乘积空间的复杂性. 现有的度量指标, 例如决策支持度、 条件熵、 最大包含度和距离度量等, 不能有效地对视角、 层次和属性进行约简, 本文在划分序乘积空间中将距离度量、 最大包含度和最大决策的概念相结合, 引入了一种新的基于距离度量最大包含熵的单调不确定性度量标准. 在此基础上, 分别定义了视角重要度、 层次重要度与属性重要度, 并且分别给出了视角、 层次和属性的约简算法, 可以对多个视角进行约简、 多个视角中的多个层次进行约简和对每个层次中的属性进行约简, 有效降低了划分序乘积空间下的复杂度. 实验结果证明了, 所提约简算法的有效性.

Gaussian graphical models (GGMs) are widely used as intuitive and efficient tools for data analysis in several application domains. To address the reproducibility issue of structure learning of a GGM, it is essential to control the false discovery rate (FDR) of the estimated edge set of the graph in terms of the graphical model. Hence, in recent years, the problem of GGM estimation with FDR control is receiving more and more attention. In this paper, we propose a new GGM estimation method by implementing multiple data splitting. Instead of using the node-by-node regressions to estimate each row of the precision matrix, we suggest directly estimating the entire precision matrix using the graphical Lasso in the multiple data splitting, and our calculation speed is $p$ times faster than the previous. We show that the proposed method can asymptotically control FDR, and the proposed method has significant advantages in computational efficiency. Finally, we demonstrate the usefulness of the proposed method through a real data analysis.

Despite of the wide use of the factor models, the issue of determining the number of factors has not been resolved in the statistics literature. An ad hoc approach is to set the number of factors to be the number of eigenvalues of the data correlation matrix that are larger than one, and subsequent statistical analysis proceeds assuming the resulting factor number is correct. In this work, we study the relation between the number of such eigenvalues and the number of factors, and provide the if and only if conditions under which the two numbers are equal. We show that the equality only relies on the properties of the loading matrix of the factor model. Guided by the newly discovered condition, we further reveal how the model error affects the estimation of the number of factors.

为了更好地突出分数阶累加算子的新息优先性以及提升波动型数据的预测精度, 提出一种新息优先分数阶累加的改进型灰色Verhulst模型. 首先, 基于Toeplitz矩阵理论详细研究了新息优先分数阶累加算子的性质, 导出了该算子满足新息优先性的条件. 其次, 给出了改进型灰色Verhulst模型的建模过程, 并用遗传算法寻求新息优先分数阶累加算子中的最优参数. 最后, 将改进型灰色Verhulst模型用于两个具有波动型数据特征的实际案例. 数值结果验证了新息优先的重要性以及本文的理论结果, 且新模型的拟合和预测精度均高于传统的灰色Verhulst 模型、 分数阶累加灰色Verhulst模型和新息优先灰色Verhulst模型.

原油的金融属性在测度原油市场风险及其与金融市场的风险溢出效应中具有重要作用.文章首先使用VMD-LZ的方法,分解并重构得到原油市场价格和风险中的金融属性成分. 进一步,文章使用MVMQ-CAViaR方法度量原油市场与金融市场在不同收益趋势和是否考虑原油金融属性等情况下的静态风险溢出.最后,文章采用DY溢出指数测算原油市场与金融市场及其不同行业之间的动态溢出指数,并分析其网络特征. 研究表明,考虑原油的金融属性后可以更好地把握原油市场风险在高低频的演化特征.进一步研究原油市场与金融市场的风险溢出效应,发现金融市场风险降低原油市场价格的波动,而原油市场风险起到促进金融市场风险的作用. 此外,收益趋势、风险趋势和金融属性对原油市场的风险溢出效应具有显著影响.