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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

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

Weighted Euclidean balancing for a matrix exposure in estimating causal effect

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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.

关键词：

Causal Effect Estimation for Multivariate Continuous Treatments

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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

A New Covariate Selection Strategy for High Dimensional data in Causal Effect Estimation with Multivariate Treatments

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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 University China

Selection of covariates is crucial in the estimation of average treatment effects given observational data with high or even ultra-high dimensional pretreatment variables. Existing methods for this problem typically assume sparse linear models for both outcome and univariate treatment, and cannot handle situations with ultra-high dimensional covariates. In this paper, we propose a new covariate selection strategy called double screening prior adaptive lasso (DSPAL) to select confounders and predictors of the outcome for multivariate treatments, which combines the adaptive lasso method with the marginal conditional (in)dependence prior information to select target covariates, in order to eliminate confounding bias and improve statistical efficiency. The distinctive features of our proposal are that it can be applied to high-dimensional or even ultra-high dimensional covariates for multivariate treatments, and can deal with the cases of both parametric and nonparametric outcome models, which makes it more robust compared to other methods. Our theoretical analyses show that the proposed procedure enjoys the sure screening property, the ranking consistency property and the variable selection consistency. Through a simulation study, we demonstrate that the proposed approach selects all confounders and predictors consistently and estimates the multivariate treatment effects with smaller bias and mean squared error compared to several alternatives under various scenarios. In real data analysis, the method is applied to estimate the causal effect of a three-dimensional continuous environmental treatment on cholesterol level and enlightening results are obtained. © 2023, CC BY.

关键词： Mean square error

Risk Indicators and its application to Capital Allocation

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SSRN

SSRN 2023年

作者： Li, Danping Wang, Ying Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University China

Capital allocation is a core task in ffnancial reporting and risk management. This paper proposes two risk indicators, which can be applied to the ffeld of capital allocation modelling. We derive the optimal conditions for these two class of risk indicators and arrive at some properties for the corresponding allocation principles. Finally, we perform numerical calculation by examples to illustrate the effects of the parameters on the optimal allocation strategies, for instance, the independent exponentials and FGM bivariate model cases. © 2023, The Authors. All rights reserved.

关键词： Risk management

Regression models of Pearson correlation coefficient

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Statistical theory and Related Fields 2023年第2期7卷 97-106页

作者： Abdisa G.Dufera Tiantian Liu Jin Xu School of Stistis East China Normal University ShanghaiPeople's Republic of China Faculty of Industial Engineering and Management Technion-Israel Institute of TechnologyHaifalsrael Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE East China Normal UniversityShanghaiPeople's Republic of China

We propose two simple regression models of Pearson correlation coefficient of two normal responses or binary responses to assess the effect of covariates of ***-based inference is established to estimate the regression coefficients,upon which bootstrap-based method is used to test the significance of covariates of *** studies show the effectiveness of the method in terms of type-I error control,power performance in moderate sample size and robustness with respect to model *** illustrate the application of the proposed method to some real data concerning health measurements.

关键词： Binary responses bivariate normal responses Pearson correlation coefficient regression

The construction of two kinds of bijections in simple random walk paths

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arXiv 2019年

作者： Song, Sai Yao, Qiang Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University NYU-ECNU Institute of Mathematical Sciences NYU Shanghai

MSC Codes 60C05, 60J10It is known that for the 2n-step symmetric simple random walk on , two events have the same probability if and only if their sets of paths have the same cardinality. In this article, we construct two kinds of bijections between sets of paths with the same cardinality. The construction is natural and simple. It can be easily realized through programming. More importantly, this construction opens a door to prove that two events in the 2n-step symmetric simple random walk on Z have the same probability and some further related results. Copyright © 2019, The Authors. All rights reserved.

关键词： Random processes

Neural Networks Perform Sufficient Dimension Reduction

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arXiv 2024年

作者： Xu, Shuntuo Yu, Zhou Key Laboratory of Advanced Theory and Application in Statistics and Data Science MOE School of Statistics East China Normal University Shanghai China

This paper investigates the connection between neural networks and sufficient dimension reduction (SDR), demonstrating that neural networks inherently perform SDR in regression tasks under appropriate rank regularizations. Specifically, the weights in the first layer span the central mean subspace. We establish the statistical consistency of the neural network-based estimator for the central mean subspace, underscoring the suitability of neural networks in addressing SDR-related challenges. Numerical experiments further validate our theoretical findings, and highlight the underlying capability of neural networks to facilitate SDR compared to the existing methods. Additionally, we discuss an extension to unravel the central subspace, broadening the scope of our investigation. © 2024, CC BY.

关键词： Neural network models

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