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A Double Varying-coefficient Modeling Approach for Analyzing Longitudinal Observations

Acta Mathematicae Applicatae Sinica 2019年第3期35卷 671-688页

作者： Qun-fang XU Rui LI School of Business Ningbo UniversityNingbo 315211China School of Statistics and Information Shanghai University of International Business and EconomicsShanghai 201620China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal UniversityMinistry of EducationShanghai 200062China

The identification of within-subject dependence is important for constructing efficient estimation in longitudinal data *** this paper,we proposed a flexible way to study this dependence by using nonparametric regression ***,we considered the estimation of varying coefficient longitudinal data model with non-stationary varying coefficient autoregressive error process over observational time *** on spline approximation and local polynomial techniques,we proposed a two-stage nonparametric estimation for unknown functional coefficients and didn’t not drop any observations in a hybrid least square loss ***,we showed that the estimated coefficient functions are asymptotically normal and derived the asymptotic biases and variances *** Carlo studies and two real applications were conducted for illustrating the performance of our proposed methods.

关键词： autoregressive process double varying coefficient irregular time quantum locally linear

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Diversity-enhanced hyper-heuristics for multi-objective dynamic flexible job shop scheduling

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Swarm and Evolutionary Computation 2025年 96卷

作者： Yuan Shi Yaoming Yang Bingdong Li Hong Qian Hao Hao Aimin Zhou Shanghai Institute of AI for Education East China Normal University Shanghai 200062 China Shanghai Frontiers Science Center of Molecule Intelligent Syntheses China School of Computer Science and Technology East China Normal University Shanghai 200062 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education Shanghai 200062 China

In the realm of multi-objective dynamic flexible job shop scheduling (MODFJSS), the prevalent reliance on genetic programming based hyper-heuristics (GPHH) has been identified as a bottleneck with quality-limited and redundant heuristics. To deal with these issues, this study introduces a novel approach named Diversity-Enhanced Hyper-Heuristics (DEHH). Our methodology encompasses three strategic thrusts: First, we introduce a multi-grained knowledge (MGK) method to represent knowledge more accurately. Second, we propose an explicit knowledge sharing (EKS) mechanism coupled with surrogate models to discern a diverse set of problem-relevant knowledge. Third, we design a multiple Pareto retrieval (MPR) mechanism to curb the proliferation of duplicate heuristics during evolution. Through comprehensive experimentation, we demonstrate that DEHH achieves superior generalization ability and diversity performance across various scenarios compared with state-of-the-art GPHH algorithms.

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The abstract of doctoral dissertation‘nonlinear wavelet density estimation and hazard rate estimation with data missing at random’

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Statistical theory and Related Fields 2020年第1期4卷 117-119页

作者： Yuye Zou Guoliang Fan Riquan Zhang School of Economics and Management Shanghai Maritime UniversityShanghaiPeople’s Republic of China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghaiPeople’s Republic of China

In this thesis,we establish non-linear wavelet density estimators and studying the asymptotic properties of the estimators with data missing at random when covariates are *** outstanding advantage of non-linear wavelet method is estimating the unsoothed functions,however,the classical kernel estimation cannot do this *** the same time,we study the larger sample properties of the ISE for hazard rate estimator.

关键词： Asymptotic normality integral square error mean integral square error missing at random non-linear wavelet

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Search for Solar Boosted Dark Matter Particles at the PandaX-4T Experiment

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Physical Review Letters 2025年第16期134卷 161003-161003页

作者： Guofang Shen Zihao Bo Wei Chen Xun Chen Yunhua Chen Zhaokan Cheng Xiangyi Cui Yingjie Fan Deqing Fang Zhixing Gao Lisheng Geng Karl Giboni Xunan Guo Xuyuan Guo Zichao Guo Chencheng Han Ke Han Changda He Jinrong He Di Huang Houqi Huang Junting Huang Ruquan Hou Yu Hou Xiangdong Ji Xiangpan Ji Yonglin Ju Chenxiang Li Jiafu Li Mingchuan Li Shuaijie Li Tao Li Zhiyuan Li Qing Lin Jianglai Liu Congcong Lu Xiaoying Lu Lingyin Luo Yunyang Luo Wenbo Ma Yugang Ma Yajun Mao Yue Meng Xuyang Ning Binyu Pang Ningchun Qi Zhicheng Qian Xiangxiang Ren Dong Shan Xiaofeng Shang Xiyuan Shao Manbin Shen Wenliang Sun Yi Tao Anqing Wang Guanbo Wang Hao Wang Jiamin Wang Lei Wang Meng Wang Qiuhong Wang Shaobo Wang Siguang Wang Wei Wang Xiuli Wang Xu Wang Zhou Wang Yuehuan Wei Weihao Wu Yuan Wu Mengjiao Xiao Xiang Xiao Kaizhi Xiong Yifan Xu Shunyu Yao Binbin Yan Xiyu Yan Yong Yang Peihua Ye Chunxu Yu Ying Yuan Zhe Yuan Youhui Yun Xinning Zeng Minzhen Zhang Peng Zhang Shibo Zhang Shu Zhang Tao Zhang Wei Zhang Yang Zhang Yingxin Zhang Yuanyuan Zhang Li Zhao Jifang Zhou Jiaxu Zhou Jiayi Zhou Ning Zhou Xiaopeng Zhou Yubo Zhou Zhizhen Zhou Haipeng An Haoming Nie School of Physics Beihang University Beijing 102206 China School of Physics and Astronomy Shanghai Jiao Tong University Key Laboratory for Particle Astrophysics and Cosmology (MoE) Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai 200240 China New Cornerstone Science Laboratory Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai 201210 China Shanghai Jiao Tong University Sichuan Research Institute Chengdu 610213 China Jinping Deep Underground Frontier Science and Dark Matter Key Laboratory of Sichuan Province China Yalong River Hydropower Development Company Ltd. 288 Shuanglin Road Chengdu 610051 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen University Zhuhai 519082 China Department of Physics Yantai University Yantai 264005 China Key Laboratory of Nuclear Physics and Ion-beam Application (MoE) Institute of Modern Physics Fudan University Shanghai 200433 China Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 China International Research Center for Nuclei and Particles in the Cosmos and Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191 China Southern Center for Nuclear-Science Theory (SCNT) Institute of Modern Physics Chinese Academy of Sciences Huizhou 516000 China SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai 200240 China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Physics University of Maryland College Park Maryland 20742 USA School of Physics Nankai University Tianjin 300071 China School of Physics Sun Yat-Sen University Guangzhou 510275 China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of China Hefei 230026 China Department of Modern Physics University of Science and Technology of China Hefei 230026 China Research Center for Particle Science and Technology

We present a novel constraint on light dark matter utilizing 1.54 metric ton/year of data acquired from the PandaX-4T dual-phase xenon time projection chamber. This constraint is derived through detecting electronic recoil signals resulting from the interaction with solar-enhanced dark matter flux. Low-mass dark matter particles, lighter than a few MeV/c2, can scatter with the thermal electrons in the Sun. Consequently, with higher kinetic energy, the boosted dark matter component becomes detectable via contact scattering with xenon electrons, resulting in a few keV energy deposition that exceeds the threshold of PandaX-4T. We calculate the expected recoil energy in PandaX-4T considering the Sun’s acceleration with heavy mediators and the detection capabilities of the xenon detector. The first experimental search results using the xenon detector yield the most stringent upper limits cross section of 3.51×10−39 cm2 at 0.08 MeV/c2 for a solar boosted dark matter mass ranging from 0.02 to 10 MeV/c2, achieving a 23-fold improvement compared with earlier experimental studies.

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Weighted Euclidean balancing for a matrix exposure in estimating causal effect

arXiv

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arXiv 2023年

作者： Chen, Juan Zhou, Yingchun Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal China

In many scientific fields such as biology, psychology and sociology, there is an increasing interest in estimating the causal effect of a matrix exposure on an outcome. Covariate balancing is crucial in causal inference and both exact balancing and approximate balancing methods have been proposed in the past decades. However, due to the large number of constraints, it is difficult to achieve exact balance or to select the threshold parameters for approximate balancing methods when the treatment is a matrix. To meet these challenges, we propose the weighted Euclidean balancing method, which approximately balance covariates from an overall perspective. This method is also applicable to high-dimensional covariates scenario. Both parametric and nonparametric methods are proposed to estimate the causal effect of matrix treatment and theoretical properties of the two estimations are provided. Furthermore, the simulation results show that the proposed method outperforms other methods in various cases. Finally, the method is applied to investigating the causal relationship between children's participation in various training courses and their IQ. The results show that the duration of attending hands-on practice courses for children at 6-9 years old has a siginificantly positive impact on children's IQ. © 2023, CC BY.

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A review of distributed statistical inference

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Statistical theory and Related Fields 2022年第2期6卷 89-99页

作者： Yuan Gao Weidong Liu Hansheng Wang Xiaozhou Wang Yibo Yan Riquan Zhang School of Statistics and Key Laboratory of Advanced Theory and Application in Statistics and Data Science–MOE East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical Sciences and Key Lab of Articial Intelligence–MOE Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China Guanghua School of Management Peking UniversityBeijingPeople’s Republic of China

The rapid emergence of massive datasets in various fields poses a serious challenge to tra-ditional statistical ***,it provides opportunities for researchers to develop novel *** by the idea of divide-and-conquer,various distributed frameworks for statistical estimation and inference have been *** were developed to deal with large-scale statistical optimization *** paper aims to provide a comprehensive review for related *** includes parametric models,nonparametric models,and other frequently used *** key ideas and theoretical properties are *** trade-off between communication cost and estimate precision together with other concerns is discussed.

关键词： Distributed computing divide-and-conquer communication-efficiency shrinkage methods nonparametric estimation principal component analysis feature screening bootstrap

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Partially Linear Single-Index Model in the Presence of Measurement Error

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Journal of Systems science & Complexity 2022年第6期35卷 2361-2380页

作者： LIN Hongmei SHI Jianhong TONG Tiejun ZHANG Riquan School of Statistics and Information Shanghai University of International Business and EconomicsShanghai 201620China Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of EducationEast China Normal UniversityShanghai 200062China School of Mathematics and Computer Science Shanxi Normal UniversityLinfen 041081China Department of Mathematics Hong Kong Baptist UniversityHongKong 519087China School of Statistics East China Normal UniversityShanghai 200062China

The partially linear single-index model(PLSIM) is a flexible and powerful model for analyzing the relationship between the response and the multivariate covariates. This paper considers the PLSIM with measurement error possibly in all the variables. The authors propose a new efficient estimation procedure based on the local linear smoothing and the simulation-extrapolation method,and further establish the asymptotic normality of the proposed estimators for both the index parameter and nonparametric link function. The authors also carry out extensive Monte Carlo simulation studies to evaluate the finite sample performance of the new method, and apply it to analyze the osteoporosis prevention data.

关键词： Local linear regression measurement error partially linear model SIMEX single-index model

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Novel loci and potential mechanisms of major depressive disorder,bipolar disorder,and schizophrenia

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science China(Life sciences) 2022年第1期65卷 167-183页

作者： He Wang Zhenghui Yi Tieliu Shi Center for Bioinformatics and Computational Biology Institute of Biomedical Sciences and School of Life SciencesEast China Normal UniversityShanghai 200241China Shanghai Mental Health Center Shanghai Jiao Tong University School of MedicineShanghai 200030China Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghai 200062China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University&Capital Medical UniversityBeijing 100083China

Different psychiatric disorders share genetic relationships and pleiotropic loci to certain *** integrated and analyzed datasets related to major depressive disorder(MDD),bipolar disorder(BIP),and schizophrenia(SCZ)from the Psychiatric Genomics Consortium using multitrait analysis of genome-wide association analysis(MTAG).MTAG significantly increased the effective sample size from 99,773 to 119,754 for MDD,from 909,061 to 1,450,972 for BIP,and from 856,677 to 940,613 for *** discovered 7,32,and 43 novel lead single nucleotide polymorphisms(SNPs)and 1,6,and 3 novel causal SNPs for MDD,BIP,and SCZ,respectively,after *** identified rs8039305 in the FURIN gene as a novel pleiotropic locus across the three *** performed marker analysis of genomic annotation(MAGMA)and Hi-C-coupled MAGMA(H-MAGMA)based gene-set analysis and identified 101 genes associated with the three disorders,which were enriched in the regulation of postsynaptic membranes,postsynaptic membrane dopaminergic synapses,and Notch signaling ***,we performed Mendelian randomization analysis using different tools and detected a causal effect of BIP on ***,we demonstrated the usage of combined genome-wide association studies summary statistics for exploring potential novel mechanisms of the three psychiatric disorders,providing an alternative approach to integrate publicly available summary data.

关键词： psychiatric disorders multitrait analysis of genome-wide association analysis novel loci Mendelian randomization causal effects

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Causal Effect Estimation for Multivariate Continuous Treatments

arXiv

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arXiv 2022年

作者： Chen, Juan Zhou, Yingchun Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University China

Causal inference is widely used in various fields, such as biology, psychology and economics, etc. In observational studies, we need to balance the covariates before estimating causal effect. This study extends the one-dimensional entropy balancing method to multiple dimensions to balance the covariates. Both parametric and nonparametric methods are proposed to estimate the causal effect of multivariate continuous treatments and theoretical properties of the two estimations are provided. Furthermore, the simulation results show that the proposed method is better than other methods in various cases. Finally, we apply the method to analyze the impact of the duration and frequency of smoking on medical expenditure. The results show that the frequency of smoking increases medical expenditure significantly while the duration of smoking does not. © 2022, CC BY.

关键词： Entropy

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Novel View Synthesis on Unpaired data by Conditional Deformable Variational Auto-Encoder 1

引用

16th European Conference on Computer Vision, ECCV 2020

作者： Yin, Mingyu Sun, Li Li, Qingli Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200241 China Key Laboratory of Advanced Theory and Application in Statistics and Data Science East China Normal University Shanghai200241 China

ISBN: (数字)9783030586041

ISBN: (纸本)9783030586034

Novel view synthesis often needs the paired data from both the source and target views. This paper proposes a view translation model under cVAE-GAN framework without requiring the paired data. We design a conditional deformable module (CDM) which uses the view condition vectors as the filters to convolve the feature maps of the main branch in VAE. It generates several pairs of displacement maps to deform the features, like the 2D optical flows. The results are fed into the deformed feature based normalization module (DFNM), which scales and offsets the main branch feature, given its deformed one as the input from the side branch. Taking the advantage of the CDM and DFNM, the encoder outputs a view-irrelevant posterior, while the decoder takes the code drawn from it to synthesize the reconstructed and the view-translated images. To further ensure the disentanglement between the views and other factors, we add adversarial training on the code. The results and ablation studies on MultiPIE and 3D chair datasets validate the effectiveness of the framework in cVAE and the designed module. © 2020, Springer Nature Switzerland AG.

关键词： Deformation

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