Wiley interdisciplinary reviews. Computational statistics最新文献

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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":"https://doi.org/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