Acta Mathematica Sinica-English Series最新文献

Finite Solvable Groups Whose Prime Graphs have Diameter 3

IF 0.8 3区数学

Acta Mathematica Sinica-English Series Pub Date : 2025-03-15 DOI: 10.1007/s10114-025-2021-4

Guohua Qian

引用次数: 0

Preface of the Special Issue on Statistics

IF 0.8 3区数学

Acta Mathematica Sinica-English Series Pub Date : 2025-02-20 DOI: 10.1007/s10114-025-4551-1

Zhiming Ma, Fuzhou Gong, Liuquan Sun

引用次数: 0

Reproducible Learning of Gaussian Graphical Models via Graphical Lasso Multiple Data Splitting

IF 0.8 3区数学

Acta Mathematica Sinica-English Series Pub Date : 2025-02-15 DOI: 10.1007/s10114-025-3324-1

Kang Hu, Danning Li, Binghui Liu

引用次数: 0

Variational Bayesian Tensor Quantile Regression

IF 0.8 3区数学

Acta Mathematica Sinica-English Series Pub Date : 2025-02-15 DOI: 10.1007/s10114-025-3390-4

Yunzhi Jin, Yanqing Zhang

{"title":"Variational Bayesian Tensor Quantile Regression","authors":"Yunzhi Jin, Yanqing Zhang","doi":"10.1007/s10114-025-3390-4","DOIUrl":"10.1007/s10114-025-3390-4","url":null,"abstract":"<div><p>Quantile regression is widely used in variable relationship research for statistical learning. Traditional quantile regression model is based on vector-valued covariates and can be efficiently estimated via traditional estimation methods. However, many modern applications involve tensor data with the intrinsic tensor structure. Traditional quantile regression can not deal with tensor regression issues well. To this end, we consider a tensor quantile regression with tensor-valued covariates and develop a novel variational Bayesian estimation approach to make estimation and prediction based on the asymmetric Laplace model and the CANDECOMP/PARAFAC decomposition of tensor coefficients. To incorporate the sparsity of tensor coefficients, we consider the multiway shrinkage priors for marginal factor vectors of tensor coefficients. The key idea of the proposed method is to efficiently combine the prior structural information of tensor and utilize the matricization of tensor decomposition to simplify the complexity of tensor coefficient estimation. The coordinate ascent algorithm is employed to optimize variational lower bound. Simulation studies and a real example show the numerical performances of the proposed method.</p></div>","PeriodicalId":50893,"journal":{"name":"Acta Mathematica Sinica-English Series","volume":"41 2","pages":"733 - 756"},"PeriodicalIF":0.8,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Kaiser Criterion in Factor Models

IF 0.8 3区数学

Acta Mathematica Sinica-English Series Pub Date : 2025-02-15 DOI: 10.1007/s10114-025-3383-3

Changhu Wang, Jianhua Guo, Yanyuan Ma, Shurong Zheng

引用次数: 0

Inference for High-Dimensional Streamed Longitudinal Data

IF 0.8 3区数学

Acta Mathematica Sinica-English Series Pub Date : 2025-02-15 DOI: 10.1007/s10114-025-3305-4

Senyuan Zheng, Ling Zhou

{"title":"Inference for High-Dimensional Streamed Longitudinal Data","authors":"Senyuan Zheng, Ling Zhou","doi":"10.1007/s10114-025-3305-4","DOIUrl":"10.1007/s10114-025-3305-4","url":null,"abstract":"<div><p>With the advent of modern devices, such as smartphones and wearable devices, high-dimensional data are collected on many participants for a period of time or even in perpetuity. For this type of data, dependencies between and within data batches exist because data are collected from the same individual over time. Under the framework of streamed data, individual historical data are not available due to the storage and computation burden. It is urgent to develop computationally efficient methods with statistical guarantees to analyze high-dimensional streamed data and make reliable inferences in practice. In addition, the homogeneity assumption on the model parameters may not be valid in practice over time. To address the above issues, in this paper, we develop a new renewable debiased-lasso inference method for high-dimensional streamed data allowing dependences between and within data batches to exist and model parameters to gradually change. We establish the large sample properties of the proposed estimators, including consistency and asymptotic normality. The numerical results, including simulations and real data analysis, show the superior performance of the proposed method.</p></div>","PeriodicalId":50893,"journal":{"name":"Acta Mathematica Sinica-English Series","volume":"41 2","pages":"757 - 779"},"PeriodicalIF":0.8,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Estimation of Single-index Models with Deep ReQU Neural Networks

IF 0.8 3区数学

Acta Mathematica Sinica-English Series Pub Date : 2025-02-15 DOI: 10.1007/s10114-025-3335-y

Zhihuang Yang, Siming Zheng, Niansheng Tang

{"title":"Efficient Estimation of Single-index Models with Deep ReQU Neural Networks","authors":"Zhihuang Yang, Siming Zheng, Niansheng Tang","doi":"10.1007/s10114-025-3335-y","DOIUrl":"10.1007/s10114-025-3335-y","url":null,"abstract":"<div><p>Single-index model offers the greater flexibility of modelling than generalized linear models and also retains the interpretability of the model to some extent. Although many standard approaches such as kernels or penalized/smooothing splines were proposed to estimate smooth link function, they cannot approximate complicated unknown link functions together with the corresponding derivatives effectively due to their poor approximation ability for a finite sample size. To alleviate this problem, this paper proposes a semiparametric least squares estimation approach for a single-index model using the rectifier quadratic unit (ReQU) activated deep neural networks, called deep semiparametric least squares (DSLS) estimation method. Under some regularity conditions, we show non-asymptotic properties of the proposed DSLS estimator, and evidence that the index coefficient estimator can achieve the semiparametric efficiency. In particular, we obtain the consistency and the convergence rate of the proposed DSLS estimator when response variable is conditionally sub-exponential. This is an attempt to incorporate deep learning technique into semiparametrically efficient estimation in a single index model. Several simulation studies and a real example data analysis are conducted to illustrate the proposed DSLS estimator.</p></div>","PeriodicalId":50893,"journal":{"name":"Acta Mathematica Sinica-English Series","volume":"41 2","pages":"640 - 676"},"PeriodicalIF":0.8,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Feature Transformation and Selection Method to Acquire an Interpretable Model Incorporating Nonlinear Effects

IF 0.8 3区数学

Acta Mathematica Sinica-English Series Pub Date : 2025-02-15 DOI: 10.1007/s10114-025-3329-9

Yu Zheng, Jin Zhu, Junxian Zhu, Xueqin Wang

{"title":"A Feature Transformation and Selection Method to Acquire an Interpretable Model Incorporating Nonlinear Effects","authors":"Yu Zheng, Jin Zhu, Junxian Zhu, Xueqin Wang","doi":"10.1007/s10114-025-3329-9","DOIUrl":"10.1007/s10114-025-3329-9","url":null,"abstract":"<div><p>Finding a highly interpretable nonlinear model has been an important yet challenging problem, and related research is relatively scarce in the current literature. To tackle this issue, we propose a new algorithm called Feat-ABESS based on a framework that utilizes feature transformation and selection for re-interpreting many machine learning algorithms. The core idea behind Feat-ABESS is to parameterize interpretable feature transformation within this framework and construct an objective function based on these parameters. This approach enables us to identify a proper interpretable feature transformation from the optimization perspective. By leveraging a recently advanced optimization technique, Feat-ABESS can obtain a concise and interpretable model. Moreover, Feat-ABESS can perform nonlinear variable selection. Our extensive experiments on 205 benchmark datasets and case studies on two datasets have demonstrated that Feat-ABESS can achieve powerful prediction accuracy while maintaining a high level of interpretability. The comparison with existing nonlinear variable selection methods exhibits Feat-ABESS has a higher true positive rate and a lower false discovery rate.</p></div>","PeriodicalId":50893,"journal":{"name":"Acta Mathematica Sinica-English Series","volume":"41 2","pages":"703 - 732"},"PeriodicalIF":0.8,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Mallows Model Averaging for Ridge Regressions

IF 0.8 3区数学

Acta Mathematica Sinica-English Series Pub Date : 2025-02-15 DOI: 10.1007/s10114-025-3409-x

Haili Zhang, Alan T. K. Wan, Kang You, Guohua Zou

引用次数: 0

Tail Dependence Matrices and Tests Based on Spearman’s ρ and Kendall’s τ

IF 0.8 3区数学

Acta Mathematica Sinica-English Series Pub Date : 2025-02-15 DOI: 10.1007/s10114-025-3225-3

Lingyue Zhang, Dawei Lu, Hengjian Cui

引用次数: 0