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Semiparametric Model Averaging for Ultrahigh-Dimensional Conditional Quantile Prediction

Acta Mathematica Sinica,English Series 2023年第6期39卷 1171-1202页

作者： Chao Hui GUO Jing LV Hu YANG Jing Wen TU Chen Xiao TIAN School of Mathematical Sciences Chongqing Normal UniversityChongqing 401331P.R.China School of Mathematics and Statistics Southwest UniversityChongqing 400715P.R.China College of Mathematics and Statistics Chongqing UniversityChongqing 401331P.R.China College of Finance University of International Business and EconomicsBeijing 100020P.R.China

In this paper,we develop a flexible semiparametric model averaging marginal regression procedure to forecast the joint conditional quantile function of the response variable for ultrahighdimensional data.First,we approximate the joint conditional quantile function by a weighted average of one-dimensional marginal conditional quantile functions that have varying coefficient structures.Then,a local linear regression technique is employed to derive the consistent estimates of marginal conditional quantile functions.Second,based on estimated marginal conditional quantile functions,we estimate and select the significant model weights involved in the approximation by a nonconvex penalized quantile regression.Under some relaxed conditions,we establish the asymptotic properties for the nonparametric kernel estimators and oracle estimators of the model averaging weights.We further derive the oracle property for the proposed nonconvex penalized model averaging procedure.Finally,simulation studies and a real data analysis are conducted to illustrate the merits of our proposed model averaging method.

关键词： kernel regression model averaging oracle property penalized quantile regression ultrahigh-dimension data

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Feature Extraction of kernel Regress Reconstruction for Fault Diagnosis Based on Self-organizing Manifold Learning

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Chinese Journal of Mechanical Engineering 2013年第5期26卷 1041-1049页

作者： CHEN Xiaoguang LIANG Lin XU Guanghua LIU Dan School of Mechanical Engineering Xi’an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University

The feature space extracted from vibration signals with various faults is often nonlinear and of high dimension.Currently,nonlinear dimensionality reduction methods are available for extracting low-dimensional embeddings,such as manifold learning.However,these methods are all based on manual intervention,which have some shortages in stability,and suppressing the disturbance noise.To extract features automatically,a manifold learning method with self-organization mapping is introduced for the first time.Under the non-uniform sample distribution reconstructed by the phase space,the expectation maximization(EM) iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention.After that,the local tangent space alignment(LTSA) algorithm is adopted to compress the high-dimensional phase space into a more truthful low-dimensional representation.Finally,the signal is reconstructed by the kernel regression.Several typical states include the Lorenz system,engine fault with piston pin defect,and bearing fault with outer-race defect are analyzed.Compared with the LTSA and continuous wavelet transform,the results show that the background noise can be fully restrained and the entire periodic repetition of impact components is well separated and identified.A new way to automatically and precisely extract the impulsive components from mechanical signals is proposed.

关键词： feature extraction manifold learning self-organize mapping kernel regression local tangent space alignment

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Background knowledge based privacy metric model for online social networks

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The Journal of China Universities of Posts and Telecommunications 2014年第2期21卷 75-82页

作者： CHENG Cheng ZHANG Chun-hong JI Yang School of Information and Communication Engineering Beijing University of Posts and Telecommunications

The data of online social network （OSN） is collected currently by the third party for various purposes. One of the problems in such practices is how to measure the privacy breach to assure users. The recent work on OSN privacy is mainly focus on privacy-preserving data publishing. However, the work on privacy metric is not systematic but mainly focus on the traditional datasets. Compared with the traditional datasets, the attribute types in OSN are more diverse and the tuple is relevant to each other. The retweet and comment make the graph character of OSN notably. Furthermore, the open application programming interfaces （APIs） and lower register barrier make OSN open environment, in which the background knowledge is more easily achieved by adversaries. This paper analyzes the background knowledge in OSN and discusses its characteristics in detail. Then a privacy metric model faces OSN background knowledge based on kernel regression is proposed. In particular, this model takes the joint attributes and link knowledge into consideration. The effect of different data distributions is discussed. The real world data set from *** has been adopted. It is demonstrated that the privacy metric algorithm in this article is effective in OSN privacy evaluation. The prediction error is 30% lower than that of the work mentioned above

关键词： privacy metric social network background knowledge kernel regression

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非参数回归在物流配送中心库存量中的应用

物流管理

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物流管理 2010年第3期 106-108页

作者：林森邱荣祖洪伟

以物流配送中心库存管理策略(R，S)中库存量预测的问题为研究对象，提出应用非参数回归方法对库存量进行拟合，用该方法与最小二乘法比较。并用非参数K邻近法则对库存量进行预测，丰富订货期制定方法。以某物流配送中心31天大米滞留库... 详细信息

以物流配送中心库存管理策略(R，S)中库存量预测的问题为研究对象，提出应用非参数回归方法对库存量进行拟合，用该方法与最小二乘法比较。并用非参数K邻近法则对库存量进行预测，丰富订货期制定方法。以某物流配送中心31天大米滞留库存量数据为例，研究结果表明，核回归模型拟合值较一元二次回归模型优异，得到较为理想的结果，实现通过统计模型客观地对物流配送中心库存信息智能化管理提供理论依据。

关键词：非参数回归核回归邻近法则库存量滞留期 non-parametric regression kernel regression k neighboring law inventory remaining period

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