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A GMM approach in coupling internal data and external summary information with heterogeneous data populations

science China Mathematics 2024年第5期67卷 1115-1132页

作者： Jun Shao Jinyi Wang Lei Wang Key Laboratory of Advanced Theory and Application in Statistics and Data Science(East China Normal University) Ministry of EducationShanghai 200062China School of Statistics East China Normal UniversityShanghai 200062China Department of Statistics University of Wisconsin-MadisonWI 53706USA School of Statistics and Data Science KLMDASRLEBPS and LPMCNankai UniversityTianjin 300071China

Because of advances in data collection and storage,statistical analysis in modern scientific research and practice now has opportunities to utilize external information such as summary statistics from similar studies.A likelihood approach based on a parametric model assumption has been developed in the literature to utilize external summary information when the populations for external and main internal data are assumed to be the *** this article,we instead consider the generalized estimation equation(GEE)approach for statistical inference,which is semiparametric or nonparametric,and show how to utilize external summary information even when internal and external data populations are not the *** approach is coupling the internal data and external summary information to form additional estimation equations and then applying the generalized method of moments(GMM).We show that the proposed GMM estimator is asymptotically normal and,under some conditions,is more efficient than the GEE estimator without using external summary *** of the asymptotic covariance matrix of the GMM estimators are also *** results are obtained to confirm our theory and quantify the improvements by utilizing external *** example is also included for illustration.

关键词： adjustment for heterogeneity constraint data integration generalized method of moments summarystatistic

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A Double Varying-coefficient Modeling Approach for Analyzing Longitudinal Observations

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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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Sparse Estimation of High-Dimensional Inverse Covariance Matrices with Explicit Eigenvalue Constraints

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Journal of the Operations Research Society of China 2021年第3期9卷 543-568页

作者： Yun-Hai Xiao Pei-Li Li Sha Lu Institute of Applied Mathematics College of Mathematics and StatisticsHenan UniversityKaifeng 475000HenanChina School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOEEast China Normal UniversityShanghai 200062China Department of Mathematics Nanning Normal UniversityNanning 530299GuangxiChina

Firstly,this paper proposes a generalized log-determinant optimization model with the purpose of estimating the high-dimensional sparse inverse covariance *** the normality assumption,the zero components in the inverse covariance matrices represent the conditional independence between pairs of variables given all the other *** generalized model considered in this study,because of the setting of the eigenvalue bounded constraints,covers a large number of existing estimators as special ***,rather than directly tracking the challenging optimization problem,this paper uses a couple of alternating direction methods of multipliers(ADMM)to solve its dual model where 5 separable structures are *** first implemented algorithm is based on a single Gauss–Seidel iteration,but it does not necessarily converge *** contrast,the second algorithm employs the symmetric Gauss–Seidel(sGS)based ADMM which is equivalent to the 2-block iterative scheme from the latest sGS decomposition ***,we do numerical simulations using the synthetic data and the real data set which show that both algorithms are very effective in estimating high-dimensional sparse inverse covariance matrix.

关键词： Non-smooth convex minimization Inverse covariance matrix Maximum likelihood estimation Augmented Lagrangian function Symmetric Gauss–Seidel iteration

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Semiparametric quantile-difference estimation for length-biased and right-censored data

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science China Mathematics 2019年第9期62卷 1823-1838页

作者： Yutao Liu Shucong Zhang Yong Zhou School of Statistics and Mathematics Central University of Finance and EconomicsBeijing100081China School of Statistics and Management Shanghai University of Finance and EconomicsShanghai200433China Key Laboratory of Advanced Theory and Application in Statistics and Data Science(MOE) Institute of Statistics and Interdisciplinary Sciences and School of StatisticsEast China Normal UniversityShanghai200241China

Prevalent cohort studies frequently involve length-biased and right-censored data, a fact that has drawn considerable attention in survival analysis. In this article, we consider survival data arising from lengthbiased sampling, and propose a new semiparametric-model-based approach to estimate quantile differences of failure time. We establish the asymptotic properties of our new estimators theoretically under mild technical conditions, and propose a resampling method for estimating their asymptotic variance. We then conduct simulations to evaluate the empirical performance and efficiency of the proposed estimators, and demonstrate their application by a real data analysis.

关键词： quantile differences length-biased sampling right-censored proportional hazards model

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Convergence rate of principal component analysis with local-linear smoother for functional data under a unified weighing scheme

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Statistical theory and Related Fields 2020年第1期4卷 55-65页

作者： Xingyu Yan Xiaolong Pu Yingchun Zhou Xiaolei Xun Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOESchool of StatisticsEast China Normal UniversityShanghaiPeople’s Republic of China School of Data Science Fudan UniversityShanghaiPeople’s Republic of China

The unified weighing scheme for the local-linear smoother in analysing functional data can deal with data that are dense,sparse or of neither *** this paper,we focus on the convergence rate of functional principal component analysis using this *** sure asymptotic consistency and rates of convergence for the estimators of eigenvalues and eigenfunctions have been *** also provide the convergence rate of the variance estimation of the measurement *** on the results,the number of observations within each curve can be of any rate relative to the sample size,which is consistent with the earlier conclusions about the asymptotic properties of the mean and covariance estimators.

关键词： Functional principal components asymptotic properties local-linear smoothing weighing scheme

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Nonparametrie Quantile Inference for Cause-specific Residual Life Function Under Length-biased Sampling

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Acta Mathematicae Applicatae Sinica 2020年第4期36卷 902-916页

作者： Fei-Peng ZHANG Cai-Yun FAN Yong ZHOU School of Economics and Finance Xi'an Jiaotong UniversityXi'an 710049China 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 MOE&Academy of Statistics and Interdisciplinary SciencesEast China Normal UniversityShanghai 200062China

This paper considers a competing risks model for right-censored and length-biased survival data from prevalent *** propose a nonparametric quantile inference procedure for cause-specific residual life distribution with competing risks *** also derive the asymptotic properties of the proposed estimators of this quantile *** studies and the unemployment data demonstrate the practical utility of the methodology.

关键词： Length-biased data competing risks quantile residual life estimating equation

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A class of admissible estimators of multiple regression coefficient with an unknown variance

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Statistical theory and Related Fields 2020年第2期4卷 190-201页

作者： Chengyuan Song Dongchu Sun Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of StatisticsEast China Normal UniversityShanghaiPeople’s Republic of China Department of Statistics University of Nebraska-LincolnLincolnNEUSA

Suppose that we observe y|θ,τ∼N_(p)(Xθ,τ^(−1)I_(p)),where θ is an unknown vector with unknown precisionτ.Estimating the regression coefficient θ with known τ has been well ***,statistical properties such as admissibility in estimating θ with unknownτare not well ***[(2009).Topics in shrinkage estimation and in causal inference(PhD thesis).Warton School,University of Pennsylvania]appears to be the first to consider the problem,developing sufficient conditions for the admissibility of estimating means of multivariate normal distributions with unknown *** generalise the sufficient conditions for admissibility and apply these results to the normal linear regression model.2-level and 3-level hierarchical models with unknown precisionτare investigated when a standard class of hierarchical priors leads to admissible estimators of θ under the normalised squared error *** reason to consider this problem is the importance of admissibility in the hierarchical prior selection,and we expect that our study could be helpful in providing some reference for choosing hierarchical priors.

关键词： Admissible estimators unknown variance multivariate normal distributions hierarchical models

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Proportional Mean Residual Life Model with Varying Coefficients for Length-Biased and Right-Censored data

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Acta Mathematica Sinica,English Series 2020年第5期36卷 578-596页

作者： Da XU Yong ZHOU Key Laboratory of Applied Statistics of MOE and School of Mathematics and Statistics Northeast Normal UniversityChangchun 130024P.R.China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOEAcademy of Statistics and Interdisciplinary SciencesEast China Normal UniversityShanghai 200062P.R.China Academy of Mathematics and Systems Sciences Chinese Academy of SciencesBeijing 100190P.R.China

Length-biased data are encountered in many fields,including economics,engineering and epidemiological cohort *** are two main challenges in the analysis of such data:the assumption of independent censoring is violated and the assumed model for the underlying population is no longer satisfied for the observed *** this paper,a proportional mean residual life varyingcoefficient model for length-biased data is considered and a local pseudo likelihood method is proposed for estimating the coefficient functions in the *** properties are investigated for the proposed *** finite sample performance of the proposed methodology is demonstrated by simulation ***,the method is applied to a real data set concerning the Academy Awards.

关键词： Length-biased data proportional hazards model mean residual life varying-coefficient model

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The kth Power Expectile Estimation and Testing

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Communications in Mathematics and statistics 2024年第4期12卷 573-615页

作者： Fuming Lin Yingying Jiang Yong Zhou College of Mathematics and Statistics Sichuan University of Science and EngineeringZigongSichuanPeople’s Republic of China South Sichuan Center for Applied Mathematics ZigongSichuanPeople’s Republic of China Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOEand Academy of Statistics and Interdisciplinary Sciences and School of StatisticsEast China Normal UniversityShanghaiPeople’s Republic of China

This paper develops the theory of the kth power expectile estimation and considers its relevant hypothesis tests for coefficients of linear regression *** prove that the asymptotic covariance matrix of kth power expectile regression converges to that of quantile regression as k converges to one and hence promise a moment estimator of asymptotic matrix of quantile *** kth power expectile regression is then utilized to test for homoskedasticity and conditional symmetry of the *** comparisons of the local power among the kth power expectile regression tests,the quantile regression test,and the expectile regression test have been *** the underlying distribution is not standard normal,results show that the optimal k are often larger than 1 and smaller than 2,which suggests the general kth power expectile regression is ***,the methods are illustrated by a real example.

关键词： The kth power expectiles Expectiles Quantiles Testing for homoskedasticity Testing for conditional symmetry Estimating asymptotic matrix of quantile regression

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