Statistica Sinica最新文献

STATISTICAL INFERENCE FOR MEAN FUNCTIONS OF COMPLEX 3D OBJECTS. 复杂三维物体平均函数的统计推断。

IF 1.2 3区数学

Statistica Sinica Pub Date : 2025-07-01 DOI: 10.5705/ss.202023.0071

Yueying Wang, Guannan Wang, Brandon Klinedinst, Auriel Willette, Li Wang

{"title":"STATISTICAL INFERENCE FOR MEAN FUNCTIONS OF COMPLEX 3D OBJECTS.","authors":"Yueying Wang, Guannan Wang, Brandon Klinedinst, Auriel Willette, Li Wang","doi":"10.5705/ss.202023.0071","DOIUrl":"10.5705/ss.202023.0071","url":null,"abstract":"The use of complex three-dimensional (3D) objects is growing in various applications as data collection techniques continue to evolve. Identifying and locating significant effects within these objects is essential for making informed decisions based on the data. This article presents an advanced nonparametric method for learning and inferring complex 3D objects, enabling accurate estimation of the underlying signals and efficient detection and localization of significant effects. The proposed method addresses the problem of analyzing irregular-shaped 3D objects by modeling them as functional data and utilizing trivariate spline smoothing based on triangulations to estimate the underlying signals. We develop a highly efficient procedure that accurately estimates the mean and covariance functions, as well as the eigenvalues and eigenfunctions. Furthermore, we rigorously establish the asymptotic properties of these estimators. Additionally, a novel approach for constructing simultaneous confidence corridors to quantify estimation uncertainty is presented, and the procedure is extended to accommodate comparisons between two independent samples. The finite-sample performance of the proposed methods is illustrated through numerical experiments and a real-data application using the Alzheimer's Disease Neuroimaging Initiative database.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":"1451-1477"},"PeriodicalIF":1.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12419769/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70939966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-dimensional Subgroup Regression Analysis. 高维亚群回归分析。

IF 1.2 3区数学

Statistica Sinica Pub Date : 2025-07-01 DOI: 10.5705/ss.202023.0075

Fei Jiang, Lu Tian, Jian Kang, Lexin Li

{"title":"High-dimensional Subgroup Regression Analysis.","authors":"Fei Jiang, Lu Tian, Jian Kang, Lexin Li","doi":"10.5705/ss.202023.0075","DOIUrl":"10.5705/ss.202023.0075","url":null,"abstract":"Classical regression generally assumes that all subjects follow a common model with the same set of parameters. With ever advancing capabilities of modern technologies to collect more subjects and more covariates, it has become increasingly common that there exist subgroups of subjects, and each group follows a different regression model with a different set of parameters. In this article, we propose a new approach for subgroup analysis in regression modeling. Specifically, we model the relation between a response and a set of primary predictors, while we explicitly model the heterogenous association given another set of auxiliary predictors, through the interaction between the primary and auxiliary variables. We introduce penalties to induce the sparsity and group structures within the regression coefficients, and to achieve simultaneous feature selection for both primary predictors that are significantly associated with the response, as well as the auxiliary predictors that define the subgroups. We establish the asymptotic guarantees in terms of parameter estimation consistency and cluster estimation consistency. We illustrate our method with an analysis of the functional magnetic resonance imaging data from the Adolescent Brain Cognitive Development Study.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"35 3","pages":"1713-1736"},"PeriodicalIF":1.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12344502/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144849473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules. 基于随机决策规则的生成对抗网络新范式。

IF 1.5 3区数学

Statistica Sinica Pub Date : 2025-04-01 DOI: 10.5705/ss.202022.0404

Sehwan Kim, Qifan Song, Faming Liang

{"title":"A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules.","authors":"Sehwan Kim, Qifan Song, Faming Liang","doi":"10.5705/ss.202022.0404","DOIUrl":"https://doi.org/10.5705/ss.202022.0404","url":null,"abstract":"The Generative Adversarial Network (GAN) was recently introduced in the literature as a novel machine learning method for training generative models. It has many applications in statistics such as nonparametric clustering and nonparametric conditional independence tests. However, training the GAN is notoriously difficult due to the issue of mode collapse, which refers to the lack of diversity among generated data. In this paper, we identify the reasons why the GAN suffers from this issue, and to address it, we propose a new formulation for the GAN based on randomized decision rules. In the new formulation, the discriminator converges to a fixed point while the generator converges to a distribution at the Nash equilibrium. We propose to train the GAN by an empirical Bayes-like method by treating the discriminator as a hyper-parameter of the posterior distribution of the generator. Specifically, we simulate generators from its posterior distribution conditioned on the discriminator using a stochastic gradient Markov chain Monte Carlo (MCMC) algorithm, and update the discriminator using stochastic gradient descent along with simulations of the generators. We establish convergence of the proposed method to the Nash equilibrium. Apart from image generation, we apply the proposed method to nonparametric clustering and nonparametric conditional independence tests. A portion of the numerical results is presented in the supplementary material.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"35 2","pages":"897-918"},"PeriodicalIF":1.5,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12017776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144028320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-response Regression for Block-missing Multi-modal Data without Imputation. 针对块缺失多模态数据的多响应回归，无需估算。

IF 1.4 3区数学

Statistica Sinica Pub Date : 2024-04-01 DOI: 10.5705/ss.202021.0170

Haodong Wang, Quefeng Li, Yufeng Liu

{"title":"Multi-response Regression for Block-missing Multi-modal Data without Imputation.","authors":"Haodong Wang, Quefeng Li, Yufeng Liu","doi":"10.5705/ss.202021.0170","DOIUrl":"10.5705/ss.202021.0170","url":null,"abstract":"Multi-modal data are prevalent in many scientific fields. In this study, we consider the parameter estimation and variable selection for a multi-response regression using block-missing multi-modal data. Our method allows the dimensions of both the responses and the predictors to be large, and the responses to be incomplete and correlated, a common practical problem in high-dimensional settings. Our proposed method uses two steps to make a prediction from a multi-response linear regression model with block-missing multi-modal predictors. In the first step, without imputing missing data, we use all available data to estimate the covariance matrix of the predictors and the cross-covariance matrix between the predictors and the responses. In the second step, we use these matrices and a penalized method to simultaneously estimate the precision matrix of the response vector, given the predictors, and the sparse regression parameter matrix. Lastly, we demonstrate the effectiveness of the proposed method using theoretical studies, simulated examples, and an analysis of a multi-modal imaging data set from the Alzheimer's Disease Neuroimaging Initiative.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":"527-546"},"PeriodicalIF":1.4,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11035992/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70937715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact Analysis for Spatial Autoregressive Models: With Application to Air Pollution in China 空间自回归模型对中国大气污染的影响分析

IF 1.4 3区数学

Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0119

Hsuan-Yu Chang, Jihai Yu

引用次数: 0

Nonlinear dimension reduction for functional data with application to clustering 函数数据非线性降维及其在聚类中的应用

IF 1.4 3区数学

Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0393

Ruoxu Tan, Yiming Zang, G. Yin

引用次数: 0

Unbiased Boosting Estimation for Censored Survival Data 删节生存数据的无偏增强估计

IF 1.4 3区数学

Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0050

Li‐Pang Chen, G. Yi

引用次数: 0

Parsimonious Tensor Discriminant Analysis 简约张量判别分析

IF 1.4 3区数学

Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202020.0496

Ning Wang, Wenjing Wang, Xin Zhang

{"title":"Parsimonious Tensor Discriminant Analysis","authors":"Ning Wang, Wenjing Wang, Xin Zhang","doi":"10.5705/ss.202020.0496","DOIUrl":"https://doi.org/10.5705/ss.202020.0496","url":null,"abstract":": Discriminant analyses of multidimensional array data (i.e., tensors) are of substantial interest in numerous statistics and engineering research problems, such as signal processing, imaging, genetics, and brain–computer interfaces. In this study, we consider a multi-class discriminant analysis with a tensor-variate predictor and a categorical response. To overcome the high dimensionality and to exploit the tensor correlation structure, we propose the discriminant analysis with tensor envelope (DATE) model for simultaneous dimension reduction and classiﬁcation. We extend the notion of tensor envelopes from regression to discriminant analysis and develop two complementary estimation procedures: DATE-L is a likelihood-based estimator that is shown to be asymptotically efﬁcient when the sample size goes to inﬁnity and the tensor dimension is ﬁxed; DATE-D is a novel decomposition-based estimator suitable for high-dimensional problems. Interestingly, we show that DATE-D is still root-n consistent, even when the tensor dimensions on each model grow arbitrarily fast, but at a similar rate. We demonstrate the robustness and efﬁ-ciency of our estimators using extensive simulations and real-data examples.","PeriodicalId":49478,"journal":{"name":"Statistica Sinica","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70936940","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 Zero-imputation Approach in Recommendation Systems with Data Missing Heterogeneously 数据异构缺失推荐系统中的零归一化方法

IF 1.4 3区数学

Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0429

Jiashen Lu, Kehui Chen

引用次数: 0

Kernel Regression Utilizing External Information as Constraints 利用外部信息作为约束的核回归

IF 1.4 3区数学

Statistica Sinica Pub Date : 2024-01-01 DOI: 10.5705/ss.202021.0446

Chi-Shian Dai, Jun Shao

引用次数: 0