Wiley interdisciplinary reviews. Computational statistics最新文献

An Introduction to Stochastic Deep Learning. 随机深度学习导论。

Wiley interdisciplinary reviews. Computational statistics Pub Date : 2026-09-01 Epub Date: 2026-09-19 DOI: 10.1002/wics.70076

Faming Liang

{"title":"An Introduction to Stochastic Deep Learning.","authors":"Faming Liang","doi":"10.1002/wics.70076","DOIUrl":"10.1002/wics.70076","url":null,"abstract":"Deep neural networks (DNNs) have achieved remarkable success in prediction, but their deterministic formulation makes many statistical inference tasks difficult. StoNet, short for stochastic neural network, addresses this limitation by reformulating a DNN as a probabilistic latent-variable model, in which the outputs of selected hidden layers or units are treated as latent variables. The article describes the formulation of StoNet and its asymptotic equivalence to a conventional DNN, showing how StoNet can serve both as a deep learning model and as an analytical device for studying DNNs. It reviews training algorithms for StoNet and discusses its applications to statistical inference problems, including nonlinear sufficient dimension reduction, causal inference with observed and unobserved confounders, Granger-causality learning for nonlinear time series, nonlinear variable selection, and prediction uncertainty quantification for large-scale DNNs. The article also discusses two related architectures: kernel-expanded StoNet and sublinearly structured DNNs, which can be viewed as structural or theoretical extensions of the StoNet framework. Under suitable architectural conditions, the former is immune to local traps in training and tends to outperform conventional DNNs in prediction; whereas the latter achieves feature-learning consistency in over-parameterized regimes when the training sample size is sufficiently large. Together, these developments show that StoNet broadens deep learning from prediction-oriented function approximation to a richer framework for statistical inference.","PeriodicalId":520388,"journal":{"name":"Wiley interdisciplinary reviews. Computational statistics","volume":"18 3","pages":"e70076"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13717249/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149303865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contrastive Dimension Reduction: A Systematic Review. 对比降维：系统回顾。

Wiley interdisciplinary reviews. Computational statistics Pub Date : 2026-06-01 Epub Date: 2026-04-19 DOI: 10.1002/wics.70065

Sam Hawke, Eric Zhang, Jiawen Chen, Didong Li

{"title":"Contrastive Dimension Reduction: A Systematic Review.","authors":"Sam Hawke, Eric Zhang, Jiawen Chen, Didong Li","doi":"10.1002/wics.70065","DOIUrl":"10.1002/wics.70065","url":null,"abstract":"Contrastive dimension reduction (CDR) methods aim to extract signal unique to or enriched in a treatment (foreground) group relative to a control (background) group. This setting arises in many scientific domains, such as genomics, imaging, and time series analysis, where traditional dimension reduction techniques such as principal component analysis (PCA) may fail to isolate the signal of interest. In this review, we provide a systematic overview of existing CDR methods. We propose a pipeline for analyzing case-control studies together with a taxonomy of CDR methods based on their assumptions, objectives, and mathematical formulations, unifying disparate approaches under a shared conceptual framework. We highlight key applications and challenges in existing CDR methods and identify open questions and future directions. By providing a clear framework for CDR and its applications, we aim to facilitate broader adoption and motivate further developments in this emerging field. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Manifold Learning Statistical and Graphical Methods of Data Analysis > Dimension Reduction Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data.","PeriodicalId":520388,"journal":{"name":"Wiley interdisciplinary reviews. Computational statistics","volume":"18 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13242285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148201727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Linear Dimensionality Reduction Methods for Analyzing Structured Biomedical Data: Existing Research and Future Opportunities. 分析结构化生物医学数据的线性降维方法：现有研究和未来机会。

Wiley interdisciplinary reviews. Computational statistics Pub Date : 2025-09-01 Epub Date: 2025-09-10 DOI: 10.1002/wics.70045

Yue Wang

{"title":"Linear Dimensionality Reduction Methods for Analyzing Structured Biomedical Data: Existing Research and Future Opportunities.","authors":"Yue Wang","doi":"10.1002/wics.70045","DOIUrl":"10.1002/wics.70045","url":null,"abstract":"High-dimensional biomedical data often exhibit complex structural features that challenge traditional analytical methods. These features include distributional structures, such as count and sparse data in single-cell RNA-seq studies; correlation structures among biomarkers, such as phylogenetic relationships in microbiome studies; and correlation structures among samples, such as spatial correlations in spatial transcriptomics. Dimensionality reduction methods that account for these structures are essential for extracting biologically meaningful insights. This article provides a selected review of existing linear dimensionality reduction methods for both supervised and unsupervised analysis of structured data. Leveraging a unified framework based on low-rank-plus-noise models, we conduct theoretical and numerical comparisons of these methods. Our review aims to equip researchers with a deeper understanding of the strengths and limitations of various structured dimensionality reduction techniques, aiding in the selection of the most suitable approach for their data. Finally, this review highlights several promising directions for future research, offering opportunities for advancements in dimensionality reduction methods tailored to the unique complexities of structured biomedical data. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical and Graphical Methods of Data Analysis > Multivariate AnalysisStatistical and Graphical Methods of Data Analysis > Dimension Reduction.","PeriodicalId":520388,"journal":{"name":"Wiley interdisciplinary reviews. Computational statistics","volume":"17 3","pages":"e70045"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671005/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vector AutoRegressive Moving Average Models: A Review. 向量自回归移动平均模型：综述。

Wiley interdisciplinary reviews. Computational statistics Pub Date : 2025-03-01 Epub Date: 2025-01-13 DOI: 10.1002/wics.70009

Marie-Christine Düker, David S Matteson, Ruey S Tsay, Ines Wilms

{"title":"Vector AutoRegressive Moving Average Models: A Review.","authors":"Marie-Christine Düker, David S Matteson, Ruey S Tsay, Ines Wilms","doi":"10.1002/wics.70009","DOIUrl":"10.1002/wics.70009","url":null,"abstract":"Vector AutoRegressive Moving Average (VARMA) models form a powerful and general model class for analyzing dynamics among multiple time series. While VARMA models encompass the Vector AutoRegressive (VAR) models, their popularity in empirical applications is dominated by the latter. Can this phenomenon be explained fully by the simplicity of VAR models? Perhaps many users of VAR models have not fully appreciated what VARMA models can provide. The goal of this review is to provide a comprehensive resource for researchers and practitioners seeking insights into the advantages and capabilities of VARMA models. We start by reviewing the identification challenges inherent to VARMA models thereby encompassing classical and modern identification schemes and we continue along the same lines regarding estimation, specification, and diagnosis of VARMA models. We then highlight the practical utility of VARMA models in terms of Granger Causality analysis, forecasting and structural analysis as well as recent advances and extensions of VARMA models to further facilitate their adoption in practice. Finally, we discuss some interesting future research directions where VARMA models can fulfill their potentials in applications as compared to their subclass of VAR models.","PeriodicalId":520388,"journal":{"name":"Wiley interdisciplinary reviews. Computational statistics","volume":"17 1","pages":"e70009"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143019986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0