Computational Statistics & Data Analysis最新文献_第10页

A goodness-of-fit test for geometric Brownian motion 几何布朗运动的拟合优度检验

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-10-01 Epub Date: 2025-04-23 DOI: 10.1016/j.csda.2025.108196

Daniel Gaigall , Philipp Wübbolding

引用次数: 0

Latent-class trajectory modeling with a heterogeneous mean-variance relation 基于异构均值-方差关系的潜类轨迹建模

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-10-01 Epub Date: 2025-05-02 DOI: 10.1016/j.csda.2025.108199

Niek G.P. Den Teuling , Francesco Ungolo , Steffen C. Pauws , Edwin R. van den Heuvel

{"title":"Latent-class trajectory modeling with a heterogeneous mean-variance relation","authors":"Niek G.P. Den Teuling , Francesco Ungolo , Steffen C. Pauws , Edwin R. van den Heuvel","doi":"10.1016/j.csda.2025.108199","DOIUrl":"10.1016/j.csda.2025.108199","url":null,"abstract":"<div><div>The benefit of addressing heteroskedastic residual variances across trajectories is investigated with the purpose of finding clusters of longitudinal trajectories. Models are proposed to account for class-specific heteroskedasticity through a mean-variance relation or random residual variance, thereby accounting for trajectory-specific variance. The analyzed latent-class trajectory models are an extension of growth mixture models (GMM). The estimation bias of the model parameters and the recoverability of the number of latent classes are assessed under various data-generating models and settings by means of a simulation study. Furthermore, the empirical applicability of these models is demonstrated through the analysis of the time-varying incidence rate of COVID-19 cases across counties in the United States. Overall, the class-specific mean-variance could be reliably estimated by the proposed models in datasets comprising 250 trajectories. In addition, the extended GMM accounting for the residual random variance showed improved group trajectory estimation over the standard GMM.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"210 ","pages":"Article 108199"},"PeriodicalIF":1.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904339","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

Co-clustering multi-view data using the Latent Block Model 使用潜在块模型的多视图数据共聚类

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-10-01 Epub Date: 2025-04-10 DOI: 10.1016/j.csda.2025.108188

Joshua Tobin , Michaela Black , James Ng , Debbie Rankin , Jonathan Wallace , Catherine Hughes , Leane Hoey , Adrian Moore , Jinling Wang , Geraldine Horigan , Paul Carlin , Helene McNulty , Anne M. Molloy , Mimi Zhang

{"title":"Co-clustering multi-view data using the Latent Block Model","authors":"Joshua Tobin , Michaela Black , James Ng , Debbie Rankin , Jonathan Wallace , Catherine Hughes , Leane Hoey , Adrian Moore , Jinling Wang , Geraldine Horigan , Paul Carlin , Helene McNulty , Anne M. Molloy , Mimi Zhang","doi":"10.1016/j.csda.2025.108188","DOIUrl":"10.1016/j.csda.2025.108188","url":null,"abstract":"<div><div>The Latent Block Model (LBM) is a prominent model-based co-clustering method, returning parametric representations of each block-cluster and allowing the use of well-grounded model selection methods. Although the LBM has been adapted to accommodate various feature types, it cannot be applied to datasets consisting of multiple distinct sets of features, termed views, for a common set of observations. The multi-view LBM is introduced herein, extending the LBM method to multi-view data, where each view marginally follows an LBM. For any pair of two views, the dependence between them is captured by a row-cluster membership matrix. A likelihood-based approach is formulated for parameter estimation, harnessing a stochastic EM algorithm merged with a Gibbs sampler, while an ICL criterion is formulated to determine the number of row- and column-clusters in each view. To justify the application of the multi-view approach, hypothesis tests are formulated to evaluate the independence of row-clusters across views, with the testing procedure seamlessly integrated into the estimation framework. A penalty scheme is also introduced to induce sparsity in row-clusterings. The algorithm's performance is validated using synthetic and real-world datasets, accompanied by recommendations for optimal parameter selection. Finally, the multi-view co-clustering method is applied to a complex genomics dataset, and is shown to provide new insights for high-dimension multi-view problems.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"210 ","pages":"Article 108188"},"PeriodicalIF":1.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868688","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

Non-parametric tests for cross-dependence based on multivariate extensions of ordinal patterns 基于有序模式多元扩展的交叉依赖非参数检验

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-10-01 Epub Date: 2025-04-10 DOI: 10.1016/j.csda.2025.108189

Angelika Silbernagel , Christian H. Weiß , Alexander Schnurr

引用次数: 0

A flexible mixed-membership model for community and enterotype detection for microbiome data 一种灵活的混合成员模型，用于微生物组数据的社区和肠型检测

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-10-01 Epub Date: 2025-04-04 DOI: 10.1016/j.csda.2025.108181

Alice Giampino, Roberto Ascari, Sonia Migliorati

{"title":"A flexible mixed-membership model for community and enterotype detection for microbiome data","authors":"Alice Giampino, Roberto Ascari, Sonia Migliorati","doi":"10.1016/j.csda.2025.108181","DOIUrl":"10.1016/j.csda.2025.108181","url":null,"abstract":"<div><div>Understanding how the human gut microbiome affects host health is challenging due to the wide interindividual variability, sparsity, and high dimensionality of microbiome data. Mixed-membership models have been previously applied to these data to detect latent communities of bacterial taxa that are expected to co-occur. The most widely used mixed-membership model is latent Dirichlet allocation (LDA). However, LDA is limited by the rigidity of the Dirichlet distribution imposed on the community proportions, which hinders its ability to model dependencies and account for overdispersion. To address this limitation, a generalization of LDA is proposed that introduces greater flexibility into the covariance matrix by incorporating the flexible Dirichlet (FD), a specific identifiable mixture with Dirichlet components. In addition to identifying communities, the new model enables the detection of enterotypes, i.e., clusters of samples with similar microbe composition. For inferential purposes, a computationally efficient collapsed Gibbs sampler that exploits the conjugacy of the FD distribution with respect to the multinomial model is proposed. A simulation study demonstrates the model's ability to accurately recover true parameter values by minimizing appropriate compositional discrepancy measures between the true and estimated values. Additionally, the model correctly identifies the number of communities, as evidenced by perplexity scores. Moreover, an application to the COMBO dataset highlights its effectiveness in detecting biologically significant and coherent communities and enterotypes, revealing a broader range of correlations between community abundances. These results underscore the new model as a definite improvement over LDA.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"210 ","pages":"Article 108181"},"PeriodicalIF":1.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792034","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

Eliciting prior information from clinical trials via calibrated Bayes factor 通过校正贝叶斯因子从临床试验中提取先验信息

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-09-01 Epub Date: 2025-03-31 DOI: 10.1016/j.csda.2025.108180

Roberto Macrì Demartino , Leonardo Egidi , Nicola Torelli , Ioannis Ntzoufras

{"title":"Eliciting prior information from clinical trials via calibrated Bayes factor","authors":"Roberto Macrì Demartino , Leonardo Egidi , Nicola Torelli , Ioannis Ntzoufras","doi":"10.1016/j.csda.2025.108180","DOIUrl":"10.1016/j.csda.2025.108180","url":null,"abstract":"<div><div>In the Bayesian framework power prior distributions are increasingly adopted in clinical trials and similar studies to incorporate external and past information, typically to inform the parameter associated with a treatment effect. Their use is particularly effective in scenarios with small sample sizes and where robust prior information is available. A crucial component of this methodology is represented by its weight parameter, which controls the volume of historical information incorporated into the current analysis. Although this parameter can be modeled as either fixed or random, eliciting its prior distribution via a full Bayesian approach remains challenging. In general, this parameter should be carefully selected to accurately reflect the available historical information without dominating the posterior inferential conclusions. A novel simulation-based calibrated Bayes factor procedure is proposed to elicit the prior distribution of the weight parameter, allowing it to be updated according to the strength of the evidence in the data. The goal is to facilitate the integration of historical data when there is agreement with current information and to limit it when discrepancies arise in terms, for instance, of prior-data conflicts. The performance of the proposed method is tested through simulation studies and applied to real data from clinical trials.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"209 ","pages":"Article 108180"},"PeriodicalIF":1.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746613","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

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 : 2025-09-01 Epub Date: 2025-03-14 DOI: 10.1016/j.csda.2025.108171

Jan-Ole Koslik

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 : 2025-09-01 Epub Date: 2025-03-18 DOI: 10.1016/j.csda.2025.108173

Filippo Pellegrino

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 : 2025-09-01 Epub 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":"2025-09-01","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

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 : 2025-09-01 Epub Date: 2025-03-25 DOI: 10.1016/j.csda.2025.108177

Yichen Lou , Yuqing Ma , Liming Xiang , Jianguo Sun

引用次数: 0