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

Linear covariance selection model via ℓ1-penalization 基于_1惩罚的线性协方差选择模型

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-03-27 DOI: 10.1016/j.csda.2025.108176

Kwan-Young Bak , Seongoh Park

{"title":"Linear covariance selection model via ℓ1-penalization","authors":"Kwan-Young Bak , Seongoh Park","doi":"10.1016/j.csda.2025.108176","DOIUrl":"10.1016/j.csda.2025.108176","url":null,"abstract":"<div><div>This paper presents a study on an <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-penalized covariance regression method. Conventional approaches in high-dimensional covariance estimation often lack the flexibility to integrate external information. As a remedy, we adopt the regression-based covariance modeling framework and introduce a linear covariance selection model (LCSM) to encompass a broader spectrum of covariance structures when covariate information is available. Unlike existing methods, we do not assume that the true covariance matrix can be exactly represented by a linear combination of known basis matrices. Instead, we adopt additional basis matrices for a portion of the covariance patterns not captured by the given bases. To estimate high-dimensional regression coefficients, we exploit the sparsity-inducing <span><math><msub><mrow><mi>ℓ</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span>-penalization scheme. Our theoretical analyses are based on the (symmetric) matrix regression model with additive random error matrix, which allows us to establish new non-asymptotic convergence rates of the proposed covariance estimator. The proposed method is implemented with the coordinate descent algorithm. We conduct empirical evaluation on simulated data to complement theoretical findings and underscore the efficacy of our approach. To show a practical applicability of our method, we further apply it to the co-expression analysis of liver gene expression data where the given basis corresponds to the adjacency matrix of the co-expression network.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"209 ","pages":"Article 108176"},"PeriodicalIF":1.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725905","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 deflation-adjusted Bayesian information criterion for selecting the number of clusters in K-means clustering 在k均值聚类中选择聚类数量的一种经通货紧缩调整的贝叶斯信息准则

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-03-26 DOI: 10.1016/j.csda.2025.108170

Masao Ueki

{"title":"A deflation-adjusted Bayesian information criterion for selecting the number of clusters in K-means clustering","authors":"Masao Ueki","doi":"10.1016/j.csda.2025.108170","DOIUrl":"10.1016/j.csda.2025.108170","url":null,"abstract":"<div><div>A deflation-adjusted Bayesian information criterion is proposed by introducing a closed-form adjustment to the variance estimate after K-means clustering. An expected lower bound of the deflation in the variance estimate after K-means clustering is derived and used as an adjustment factor for the variance estimates. The deflation-adjusted variance estimates are applied to the Bayesian information criterion under the Gaussian model for selecting the number of clusters. The closed-form expression makes the proposed deflation-adjusted Bayesian information criterion computationally efficient. Simulation studies show that the deflation-adjusted Bayesian information criterion performs better than other existing clustering methods in some situations, including K-means clustering with the number of clusters selected by standard Bayesian information criteria, the gap statistic, the average silhouette score, the prediction strength, and clustering using a Gaussian mixture model with the Bayesian information criterion. The proposed method is illustrated through a real data application for clustering human genomic data from the 1000 Genomes Project.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"209 ","pages":"Article 108170"},"PeriodicalIF":1.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738691","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 multiple imputation approach for flexible modelling of interval-censored data with missing and censored covariates 一种具有缺失协变量和被删协变量的区间截尾数据灵活建模的多重插值方法

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-03-25 DOI: 10.1016/j.csda.2025.108177

Yichen Lou , Yuqing Ma , Liming Xiang , Jianguo Sun

引用次数: 0

Sparse factor analysis for categorical data with the group-sparse generalized singular value decomposition 基于群稀疏广义奇异值分解的分类数据稀疏因子分析

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-03-25 DOI: 10.1016/j.csda.2025.108179

Ju-Chi Yu , Julie Le Borgne , Anjali Krishnan , Arnaud Gloaguen , Cheng-Ta Yang , Laura A. Rabin , Hervé Abdi , Vincent Guillemot

{"title":"Sparse factor analysis for categorical data with the group-sparse generalized singular value decomposition","authors":"Ju-Chi Yu , Julie Le Borgne , Anjali Krishnan , Arnaud Gloaguen , Cheng-Ta Yang , Laura A. Rabin , Hervé Abdi , Vincent Guillemot","doi":"10.1016/j.csda.2025.108179","DOIUrl":"10.1016/j.csda.2025.108179","url":null,"abstract":"<div><div>Correspondence analysis, multiple correspondence analysis, and their discriminant counterparts (i.e., discriminant simple correspondence analysis and discriminant multiple correspondence analysis) are methods of choice for analyzing multivariate categorical data. In these methods, variables are integrated into optimal components computed as linear combinations whose weights are obtained from a generalized singular value decomposition (GSVD) that integrates specific metric constraints on the rows and columns of the original data matrix. The weights of the linear combinations are, in turn, used to interpret the components, and this interpretation is facilitated when components are 1) pairwise orthogonal and 2) when the values of the weights are either large or small but not intermediate—a configuration called a simple or a sparse structure. To obtain such simple configurations, the optimization problem solved by the GSVD is extended to include new constraints that implement component orthogonality and sparse weights. Because multiple correspondence analysis represents qualitative variables by a set of binary columns in the data matrix, an additional group constraint is added to the optimization problem in order to sparsify the whole set of columns representing one qualitative variable. This method—called group-sparse GSVD (gsGSVD)—integrates these constraints in a new algorithm via an iterative projection scheme onto the intersection of subspaces where each subspace implements a specific constraint. This algorithm is described in details, and we show how it can be adapted to the sparsification of simple and multiple correspondence analysis (as well as their barycentric discriminant analysis versions). This algorithm is illustrated with the analysis of four different data sets—each illustrating the sparsification of a particular CA-based method.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"209 ","pages":"Article 108179"},"PeriodicalIF":1.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769158","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

Selecting time-series hyperparameters with the artificial jackknife 用人工折刀选择时间序列超参数

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-03-18 DOI: 10.1016/j.csda.2025.108173

Filippo Pellegrino

引用次数: 0

Hidden semi-Markov models with inhomogeneous state dwell-time distributions 具有非齐次状态驻留时间分布的隐半马尔可夫模型

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-03-14 DOI: 10.1016/j.csda.2025.108171

Jan-Ole Koslik

引用次数: 0

Model-based edge clustering for weighted networks with a noise component 基于模型的带噪声加权网络边缘聚类

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-03-14 DOI: 10.1016/j.csda.2025.108172

Haomin Li, Daniel K. Sewell

{"title":"Model-based edge clustering for weighted networks with a noise component","authors":"Haomin Li, Daniel K. Sewell","doi":"10.1016/j.csda.2025.108172","DOIUrl":"10.1016/j.csda.2025.108172","url":null,"abstract":"<div><div>Clustering is a fundamental task in network analysis, essential for uncovering hidden structures within complex systems. Edge clustering, which focuses on relationships between nodes rather than the nodes themselves, has gained increased attention in recent years. However, existing edge clustering algorithms often overlook the significance of edge weights, which can represent the strength or capacity of connections, and fail to account for noisy edges—connections that obscure the true structure of the network. To address these challenges, the Weighted Edge Clustering Adjusting for Noise (WECAN) model is introduced. This novel algorithm integrates edge weights into the clustering process and includes a noise component that filters out spurious edges. WECAN offers a data-driven approach to distinguishing between meaningful and noisy edges, avoiding the arbitrary thresholding commonly used in network analysis. Its effectiveness is demonstrated through simulation studies and applications to real-world datasets, showing significant improvements over traditional clustering methods. Additionally, the R package “WECAN”<span><span><sup>1</sup></span></span> has been developed to facilitate its practical implementation.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"209 ","pages":"Article 108172"},"PeriodicalIF":1.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644209","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

Functional nonlinear principal component analysis 泛函非线性主成分分析

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-03-13 DOI: 10.1016/j.csda.2025.108169

Qingzhi Zhong , Xinyuan Song

{"title":"Functional nonlinear principal component analysis","authors":"Qingzhi Zhong , Xinyuan Song","doi":"10.1016/j.csda.2025.108169","DOIUrl":"10.1016/j.csda.2025.108169","url":null,"abstract":"<div><div>The widely adopted dimension reduction technique, functional principal component analysis (FPCA), typically represents functional data as a linear combination of functional principal components (FPCs) and their corresponding scores. However, this linear formulation is too restrictive to reflect reality because it fails to capture the nonlinear dependence of functional data when nonlinear features are present in the data. This study develops a novel FPCA model to uncover the nonlinear structures of functional data. The proposed method can accommodate multivariate functional data observed on different domains, and multidimensional functional data with gaps and holes. To navigate the complexities of spatial structure in multidimensional functional variables, tensor product smoothing and spline smoothing over triangulation are employed, providing precise tools for approximating nonparametric function. Furthermore, an efficient estimation approach and theory are developed when the number of FPCs diverges to infinity. To assess its performance comprehensively, extensive simulations are conducted, and the proposed method is applied to real data from the Alzheimer's Disease Neuroimaging Initiative study, affirming its practical efficacy in uncovering and interpreting nonlinear structures inherent in functional data.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"209 ","pages":"Article 108169"},"PeriodicalIF":1.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644148","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

Manifold-valued models for analysis of EEG time series data 脑电时序数据分析的流形值模型

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-03-07 DOI: 10.1016/j.csda.2025.108168

Tao Ding , Tom M.W. Nye , Yujiang Wang

{"title":"Manifold-valued models for analysis of EEG time series data","authors":"Tao Ding , Tom M.W. Nye , Yujiang Wang","doi":"10.1016/j.csda.2025.108168","DOIUrl":"10.1016/j.csda.2025.108168","url":null,"abstract":"<div><div>EEG (electroencephalogram) records brain electrical activity and is a vital clinical tool in the diagnosis and treatment of epilepsy. Time series of covariance matrices between EEG channels for patients suffering from epilepsy, obtained from an open-source dataset, are analysed. The aim is two-fold: to develop a model with interpretable parameters for different possible modes of EEG dynamics, and to explore the extent to which modelling results are affected by the choice of geometry imposed on the space of covariance matrices. The space of full-rank covariance matrices of fixed dimension forms a smooth manifold, and any statistical analysis inherently depends on the choice of metric or Riemannian structure on this manifold. The model specifies a distribution for the tangent direction vector at any time point, combining an autoregressive term, a mean reverting term and a form of Gaussian noise. Parameter inference is performed by maximum likelihood estimation, and we compare modelling results obtained using the standard Euclidean geometry and the affine invariant geometry on covariance matrices. The findings reveal distinct dynamics between epileptic seizures and interictal periods (between seizures), with interictal series characterized by strong mean reversion and absence of autoregression, while seizures exhibit significant autoregressive components with weaker mean reversion. The fitted models are also used to measure seizure dissimilarity within and between patients.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"209 ","pages":"Article 108168"},"PeriodicalIF":1.5,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680033","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

Regression analysis of elliptically symmetric directional data 椭圆对称方向数据的回归分析

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-03-03 DOI: 10.1016/j.csda.2025.108167

Zehao Yu, Xianzheng Huang

引用次数: 0