Computational Statistics & Data Analysis最新文献_第8页

Model-based clustering for covariance matrices via penalized Wishart mixture models 基于模型的基于惩罚Wishart混合模型的协方差矩阵聚类

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-12-01 Epub Date: 2025-06-20 DOI: 10.1016/j.csda.2025.108232

Andrea Cappozzo , Alessandro Casa

{"title":"Model-based clustering for covariance matrices via penalized Wishart mixture models","authors":"Andrea Cappozzo , Alessandro Casa","doi":"10.1016/j.csda.2025.108232","DOIUrl":"10.1016/j.csda.2025.108232","url":null,"abstract":"<div><div>Covariance matrices provide a valuable source of information about complex interactions and dependencies within the data. However, from a clustering perspective, this information has often been underutilized and overlooked. Indeed, commonly adopted distance-based approaches tend to rely primarily on mean levels to characterize and differentiate between groups. Recently, there have been promising efforts to cluster covariance matrices directly, thereby distinguishing groups solely based on the relationships between variables. From a model-based perspective, a probabilistic formalization has been provided by considering a mixture model with component densities following a Wishart distribution. Notwithstanding, this approach faces challenges when dealing with a large number of variables, as the number of parameters to be estimated increases quadratically. To address this issue, a sparse Wishart mixture model is proposed, which assumes that the component scale matrices possess a cluster-dependent degree of sparsity. Model estimation is performed by maximizing a penalized log-likelihood, enforcing a covariance graphical lasso penalty on the component scale matrices. This penalty not only reduces the number of non-zero parameters, mitigating the challenges of high-dimensional settings, but also enhances the interpretability of results by emphasizing the most relevant relationships among variables. The proposed methodology is tested on both simulated and real data, demonstrating its ability to unravel the complexities of neuroimaging data and effectively cluster subjects based on the relational patterns among distinct brain regions.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"212 ","pages":"Article 108232"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338394","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

Enhancing approximate modular Bayesian inference by emulating the conditional posterior 通过模拟条件后验增强近似模贝叶斯推理

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-12-01 Epub Date: 2025-06-20 DOI: 10.1016/j.csda.2025.108235

Grant Hutchings , Kellin N. Rumsey , Derek Bingham , Gabriel Huerta

{"title":"Enhancing approximate modular Bayesian inference by emulating the conditional posterior","authors":"Grant Hutchings , Kellin N. Rumsey , Derek Bingham , Gabriel Huerta","doi":"10.1016/j.csda.2025.108235","DOIUrl":"10.1016/j.csda.2025.108235","url":null,"abstract":"<div><div>In modular Bayesian analyses, complex models are composed of distinct modules, each representing different aspects of the data or prior information. In this context, fully Bayesian approaches can sometimes lead to undesirable feedback between modules, compromising the integrity of the inference. The “cut-distribution” prevents unwanted influence between modules by “cutting” feedback. The direct sampling (DS) algorithm is standard practice for approximating the cut-distribution, but it can be computationally intensive, especially when the number of imputations required is large. An enhanced method is proposed, the Emulating the Conditional Posterior (ECP) algorithm, which leverages emulation to increase the number of imputations. Through numerical experiment it is demonstrated that the ECP algorithm outperforms the traditional DS approach in terms of accuracy and computational efficiency, particularly when resources are constrained. It is also shown how the DS algorithm can be improved using ideas from design of experiments. Some practical recommendations are given for algorithm choice in modular Bayesian analyses.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"212 ","pages":"Article 108235"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518106","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

Privacy-preserving communication-efficient spectral clustering for distributed multiple networks 分布式多网络的保密性通信高效频谱聚类

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-12-01 Epub Date: 2025-06-09 DOI: 10.1016/j.csda.2025.108230

Shanghao Wu , Xiao Guo , Hai Zhang

引用次数: 0

A score-based threshold effect test in time series models 时间序列模型中基于分数的阈值效应检验

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-12-01 Epub Date: 2025-06-25 DOI: 10.1016/j.csda.2025.108236

Shufang Wei , Yaping Deng , Yaxing Yang

引用次数: 0

JANE: Just Another latent space NEtwork clustering algorithm 简：只是另一个潜在空间网络聚类算法

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-11-01 Epub Date: 2025-06-02 DOI: 10.1016/j.csda.2025.108228

Alan T. Arakkal, Daniel K. Sewell

{"title":"JANE: Just Another latent space NEtwork clustering algorithm","authors":"Alan T. Arakkal, Daniel K. Sewell","doi":"10.1016/j.csda.2025.108228","DOIUrl":"10.1016/j.csda.2025.108228","url":null,"abstract":"<div><div>While latent space network models have been a popular approach for community detection for over 15 years, major computational challenges remain, limiting the ability to scale beyond small networks. The R statistical software package, <span>JANE</span>, introduces a new estimation algorithm with massive speedups derived from: (1) a low dimensional approximation approach to adjust for degree heterogeneity parameters; (2) an approximation of intractable likelihood terms; (3) a fast initialization algorithm; and (4) a novel set of convergence criteria focused on clustering performance. Additionally, the proposed method addresses limitations of current implementations, which rely on a restrictive spherical-shape assumption for the prior distribution on the latent positions; relaxing this constraint allows for greater flexibility across diverse network structures. A simulation study evaluating clustering performance of the proposed approach against state-of-the-art methods shows dramatically improved clustering performance in most scenarios and significant reductions in computational time — up to 45 times faster compared to existing approaches.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"211 ","pages":"Article 108228"},"PeriodicalIF":1.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222027","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

Region detection and image clustering via sparse Kronecker product decomposition 基于稀疏Kronecker积分解的区域检测与图像聚类

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-11-01 Epub Date: 2025-06-03 DOI: 10.1016/j.csda.2025.108226

Guang Yang , Long Feng

{"title":"Region detection and image clustering via sparse Kronecker product decomposition","authors":"Guang Yang , Long Feng","doi":"10.1016/j.csda.2025.108226","DOIUrl":"10.1016/j.csda.2025.108226","url":null,"abstract":"<div><div>Image clustering is usually conducted by vectorizing image pixels, treating them as independent, and applying classical clustering approaches to the obtained features. However, as image data is often of high-dimensional and contains rich spatial information, such treatment is far from satisfactory. For medical image data, another important characteristic is the region-wise sparseness in signals. That is to say, there are only a few unknown regions in the medical image that differentiate the images associated with different groups of patients, while other regions are uninformative. Accurately detecting these informative regions would not only improve clustering accuracy, more importantly, it would also provide interpretations for the rationale behind them. Motivated by the need to identify significant regions of interest, we propose a general framework named Image Clustering via Sparse Kronecker Product Decomposition (IC-SKPD). This framework aims to simultaneously divide samples into clusters and detect regions that are informative for clustering. Our framework is general in the sense that it provides a unified treatment for matrix and tensor-valued samples. An iterative hard-thresholded singular value decomposition approach is developed to solve this model. Theoretically, the IC-SKPD enjoys guarantees for clustering accuracy and region detection consistency under mild conditions on the minimum signals. Comprehensive simulations along with real data analysis further validate the superior performance of IC-SKPD on clustering and region detection.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"211 ","pages":"Article 108226"},"PeriodicalIF":1.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144242892","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 Dirichlet stochastic block model for composition-weighted networks 组合加权网络的Dirichlet随机块模型

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-11-01 Epub Date: 2025-05-16 DOI: 10.1016/j.csda.2025.108204

Iuliia Promskaia , Adrian O'Hagan , Michael Fop

{"title":"A Dirichlet stochastic block model for composition-weighted networks","authors":"Iuliia Promskaia , Adrian O'Hagan , Michael Fop","doi":"10.1016/j.csda.2025.108204","DOIUrl":"10.1016/j.csda.2025.108204","url":null,"abstract":"<div><div>Network data are prevalent in applications where individual entities interact with each other, and often these interactions have associated weights representing the strength of association. Clustering such weighted network data is a common task, which involves identifying groups of nodes that display similarities in the way they interact. However, traditional clustering methods typically use edge weights in their raw form, overlooking that the observed weights are influenced by the nodes' capacities to distribute weights along the edges. This can lead to clustering results that primarily reflect nodes' total weight capacities rather than the specific interactions between them. One way to address this issue is to analyse the strengths of connections in relative rather than absolute terms, by transforming the relational weights into a compositional format. This approach expresses each edge weight as a proportion of the sending or receiving weight capacity of the respective node. To cluster these data, a Dirichlet stochastic block model tailored for composition-weighted networks is proposed. The model relies on direct modelling of compositional weight vectors using a Dirichlet mixture, where parameters are determined by the cluster labels of sender and receiver nodes. Inference is implemented via an extension of the classification expectation-maximisation algorithm, expressing the complete data likelihood of each node as a function of fixed cluster labels of the remaining nodes. A model selection criterion is derived to determine the optimal number of clusters. The proposed approach is validated through simulation studies, and its practical utility is illustrated on two real-world networks.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"211 ","pages":"Article 108204"},"PeriodicalIF":1.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144090449","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

Adaptive-to-sub-null testing for mediation effects in structural equation models 结构方程模型中中介效应的自适应亚零检验

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-11-01 Epub Date: 2025-05-20 DOI: 10.1016/j.csda.2025.108205

Jiaqi Huang , Chuyun Ye , Lixing Zhu

{"title":"Adaptive-to-sub-null testing for mediation effects in structural equation models","authors":"Jiaqi Huang , Chuyun Ye , Lixing Zhu","doi":"10.1016/j.csda.2025.108205","DOIUrl":"10.1016/j.csda.2025.108205","url":null,"abstract":"<div><div>To effectively implement large-scale hypothesis testing of causal mediation effects and control false discovery rate (FDR) for linear structural equation models, this paper proposes an Adaptive-to-Sub-Null test (AtST) tailored specifically for the assessment of multidimensional mediation effects. The significant distinction of AtST from existing methods is that for every mediator, the weak limits of the test statistic under all mutually exclusive sub-null hypotheses uniformly conform to a chi-square distribution with one degree of freedom. Therefore, in the asymptotic sense, the significance level can be maintained and the <em>p</em>-values can be computed easily without any other prior information on the sub-null hypotheses or resampling technique. In theoretical investigations, we extend existing parameter estimation methods by allowing lower sparsity level in high-dimensional covariate vectors. These results offer a solid base for better FDR control by directly applying the classical Storey's method. We also apply a data-driven approach for selecting the tuning parameter of Storey's estimator. Simulations are conducted to demonstrate the efficacy and validity of the AtST, complemented by an analytical exploration of a genuine dataset for illustration.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"211 ","pages":"Article 108205"},"PeriodicalIF":1.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116906","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

Monotone composite quantile regression neural network for censored data with a cure fraction 带固定分数的删减数据的单调复合分位数回归神经网络

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-11-01 Epub Date: 2025-05-08 DOI: 10.1016/j.csda.2025.108201

Xinran Zhang , Xiaohui Yuan , Chunjie Wang , Xinyuan Song

引用次数: 0

Statistical inference for partially shape-constrained function-on-scalar linear regression models 部分形状约束标量函数线性回归模型的统计推断

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-11-01 Epub Date: 2025-05-12 DOI: 10.1016/j.csda.2025.108200

Kyunghee Han , Yeonjoo Park , Soo-Young Kim

{"title":"Statistical inference for partially shape-constrained function-on-scalar linear regression models","authors":"Kyunghee Han , Yeonjoo Park , Soo-Young Kim","doi":"10.1016/j.csda.2025.108200","DOIUrl":"10.1016/j.csda.2025.108200","url":null,"abstract":"<div><div>Functional linear regression models are widely used to link functional/longitudinal outcomes with multiple scalar predictors, identifying time-varying covariate effects through regression coefficient functions. Beyond assessing statistical significance, characterizing the shapes of coefficient functions is crucial for drawing interpretable scientific conclusions. Existing studies on shape-constrained analysis primarily focus on global shapes, which require strict prior knowledge of functional relationships across the entire domain. This often leads to misspecified regression models due to a lack of prior information, making them impractical for real-world applications. To address this, a flexible framework is introduced to identify partial shapes in regression coefficient functions. The proposed partial shape-constrained analysis enables researchers to validate functional shapes within a targeted sub-domain, avoiding the misspecification of shape constraints outside the sub-domain of interest. The method also allows for testing different sub-domains for individual covariates and multiple partial shape constraints across composite sub-domains. Our framework supports both kernel- and spline-based estimation approaches, ensuring robust performance with flexibility in computational preference. Finite-sample experiments across various scenarios demonstrate that the proposed framework significantly outperforms the application of global shape constraints to partial domains in both estimation and inference procedures. The inferential tool particularly maintains the type I error rate at the nominal significance level and exhibits increasing power with larger sample sizes, confirming the consistency of the test procedure. The practicality of partial shape-constrained inference is demonstrated through two applications: a clinical trial on NeuroBloc for type A-resistant cervical dystonia and the National Institute of Mental Health Schizophrenia Study.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"211 ","pages":"Article 108200"},"PeriodicalIF":1.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083910","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