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Distributed Penalized Modal Regression for massive data

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Journal of Systems Science & Complexity 2023年第2期36卷 798-821页

作者： JIN Jun LIU Shuangzhe MA Tiefeng College of Mathematical Sciences Yangzhou UniversityYangzhou 225002China Faculty of Science and Technology University of CanberraACT 2601Australia School of Statistics Southwestern University of Finance and EconomicsChengdu 661130China

Nowadays,researchers are frequently confronted with challenges from massive data computing by a number of limitations of computer primary *** regression(MR)is a good alternative of the mean regression and likelihood based methods,because of its robustness and high *** this end,the authors extend MR to massive data analysis and propose a computationally and statistically efficient divide and conquer MR method(DC-MR).The major novelty of this method consists of splitting one entire dataset into several blocks,implementing the MR method on data in each block,and deriving final results through combining these regression results via a weighted average,which provides approximate estimates of regression results on the entire *** proposed method significantly reduces the required amount of primary memory,and the resulting estimator is theoretically as efficient as the traditional MR on the entire data *** authors also investigate a multiple hypothesis testing variable selection approach to select significant parametric components and prove the approach possessing the oracle *** addition,the authors propose a practical modified modal expectation-maximization(MEM)algorithm for the proposed *** studies on simulated and real datasets are conducted to assess and showcase the practical and effective performance of our proposed methods.

关键词： Asymptotic distribution divide and conquer massive data modal regression multiple hypothesis testing

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Science China Mathematics 2024年第2期67卷 405-430页

作者： Junzhuo Gao Lei Wang Heng Lian School of Statistics and data Science&LPMC Nankai UniversityTianjin 300071China Department of Mathematics City University of Hong KongHong KongChina

In this paper, we consider the unified optimal subsampling estimation and inference on the lowdimensional parameter of main interest in the presence of the nuisance parameter for low/high-dimensionalgeneralized linear models (GLMs) with massive data. We first present a general subsampling decorrelated scorefunction to reduce the influence of the less accurate nuisance parameter estimation with the slow convergencerate. The consistency and asymptotic normality of the resultant subsample estimator from a general decorrelatedscore subsampling algorithm are established, and two optimal subsampling probabilities are derived under theA- and L-optimality criteria to downsize the data volume and reduce the computational burden. The proposedoptimal subsampling probabilities provably improve the asymptotic efficiency of the subsampling schemes in thelow-dimensional GLMs and perform better than the uniform subsampling scheme in the high-dimensional GLMs.A two-step algorithm is further proposed to implement, and the asymptotic properties of the correspondingestimators are also given. Simulations show satisfactory performance of the proposed estimators, and twoapplications to census income and Fashion-MNIST datasets also demonstrate its practical applicability.

关键词： A-optimality decorrelated score subsampling high-dimensional inference L-optimality massive data

massive data Covert Transmission Scheme Based on Shamir Threshold

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Wuhan University Journal of Natural Sciences 2010年第3期15卷 227-231页

作者： ZHANG Tao WANG Yadi RONG Xing Institute of Electronic Technology Information Engineering University Zhengzhou 450004 Henan China

massive data covert transmission scheme based on Shamir threshold is proposed in this paper. This method applies Shamir threshold scheme to divide data, uses information hiding technology to cover shadows, and realizes massive data covert transmission through transmitting stego-covers. Analysis proves that compared with the natural division method, this scheme not only improves the time-efficiency of transmitting but also enhances the security.

关键词： information hiding and transmission Shamir threshold scheme massive data time-efficiency security

Semiparametric Likelihood-based Inference for Censored data with Auxiliary Information from External massive data Sources

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Acta Mathematicae Applicatae Sinica 2020年第3期36卷 642-656页

作者： Yue-xin FANG Yong ZHOU School of Statistics and Management Shanghai University of Finance and EconomicsShanghai200433China Key Laboratory of Advanced Theory and Application in Statistics and data Science MOEand Academy of Statistics and Interdisciplinary SciencesEast China Normal UniversityShanghai 200062China

Published auxiliary information can be helpful in conducting statistical inference in a new *** this paper,we synthesize the auxiliary information with semiparametric likelihood-based inference for censoring data with the total sample size is *** express the auxiliary information as constraints on the regression coefficients and the covariate distribution,then use empirical likelihood method for general estimating equations to improve the efficiency of the interested parameters in the specified *** consistency and asymptotic normality of the resulting regression parameter estimators *** numerical simulation and application with different supposed conditions show that the proposed method yields a substantial gain in efficiency of the interested parameters.

关键词： Auxiliary information massive data Censored data Empirical likelihood Estimation equations

A study on fast post-processing massive data of casting numerical simulation on personal computers

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China Foundry 2013年第5期10卷 321-324页

作者： Chen Tao Liao Dunming Pang Shenyong Zhou Jianxin State Key Laboratory of Materials Processing and Die & Mould Technology Huazhong University of Science and Technology

When castings become complicated and the demands for precision of numerical simulation become higher,the numerical data of casting numerical simulation become more *** a general personal computer,these massive numerical data may probably exceed the capacity of available memory,resulting in failure of *** on the out-of-core technique,this paper proposes a method to effectively utilize external storage and reduce memory usage dramatically,so as to solve the problem of insufficient memory for massive data rendering on general personal *** on this method,a new postprocessor is *** is capable to illustrate filling and solidification processes of casting,as well as thermal *** new post-processor also provides fast interaction to simulation *** analysis as well as several practical examples prove that the memory usage and loading time of the post-processor are independent of the size of the relevant files,but the proportion of the number of cells on ***,the speed of rendering and fetching of value from the mouse is appreciable,and the demands of real-time and interaction are satisfied.

关键词： casting numerical simulation massive data fast post-processing

Linear expectile regression under massive data

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Fundamental Research 2021年第5期1卷 574-585页

作者： Shanshan Song Yuanyuan Lin Yong Zhou Department of Statistics Chinese University of Hong KongHongKongChina Academy of Statistics and Interdisciplinary Sciences East China Normal UniversityShanghaiChina

In this paper,we study the large-scale inference for a linear expectile regression *** mitigate the computational challenges in the classical asymmetric least squares(ALS)estimation under massive data,we propose a communication-efficient divide and conquer algorithm to combine the information from sub-machines through confidence *** resulting pooled estimator has a closed-form expression,and its consistency and asymptotic normality are established under mild ***,we derive the Bahadur representation of the ALS estimator,which serves as an important tool to study the relationship between the number of submachines K and the sample *** studies including both synthetic and real data examples are presented to illustrate the finite-sample performance of our method and support the theoretical results.

关键词： Divide and conquer algorithm Expectile regression (Asymptotic)confidence distribution massive data

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Research on data load balancing technology of massive storage systems for wearable devices

2011 IEEE International Conference on Computer Science and Automation Engineering(CSAE 2011)

作者： Zhiqiong Wang Hao Chen Yalu Huang Yu Xu Longwei Yang Jingjing Xiao Sino-Dutch Biomedical and Information Engineering School Northeastern University Shenyang China

As biomedical data grows rapidly nowadays,we are going to study how to efficiently apply cloud computing into biomedical *** this paper,the process of solving biomedical problem based on cloud computing is ***,the biomedical issue would be focus on the gene match and generation of large amount of the emulated data from real ***,a mathematical model is proposed to describe the ***,the mathematical model is implemented into our MapReduce model,which means simulating the biomedical application rested on cloud ***,some experiments are conducted to test the performance of cloud computing on this ***,at the end of the paper,some advices are given to doctors and biomedical scientists.

关键词： Cloud computing massive data Similarity Gene

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Digital Communications and Networks 2022年第2期8卷 143-149页

作者： Shujun Liang Jing Cheng Jianwei Zhang Software Engineering College Zhengzhou University of Light IndustryZhengzhou450000China Engineering Training Center Zhengzhou University of Light Industry Zhengzhou450000China

Because of the limited memory of the increasing amount of information in current wearable devices,the processing capacity of the servers in the storage system can not keep up with the speed of information growth,resulting in low load balancing,long load balancing time and data processing ***,a data load balancing technology is applied to the massive storage systems of wearable devices in this *** first analyze the object-oriented load balancing method,and formally describe the dynamic load balancing issues,taking the load balancing as a mapping ***,the task of assigning each data node and the request of the corresponding data node’s actual processing capacity are *** data is allocated to the corresponding data storage node to complete the calculation of the comprehensive weight of the data storage *** to the load information of each data storage node collected by the scheduler in the storage system,the load weight of the current data storage node is calculated and *** data load balancing of the massive storage system for wearable devices is *** experimental results show that the average time of load balancing using this method is 1.75h,which is much lower than the traditional *** results show the data load balancing technology of the massive storage system of wearable devices has the advantages of short data load balancing time,high load balancing,strong data processing capability,short processing time and obvious application.

关键词： Wearable device massive data data storage system Load balancing Weigh

The organization and scheduling of massive grid geographic data

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The organization and scheduling of massive grid geographic d...