Computational Statistics & Data Analysis最新文献_第2页

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 : 2025-05-02 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":"2025-05-02","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

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 : 2025-04-23 DOI: 10.1016/j.csda.2025.108196

Daniel Gaigall , Philipp Wübbolding

引用次数: 0

A simultaneous confidence-bounded true discovery proportion perspective on localizing differences in smooth terms in regression models 回归模型中平滑项的局部化差异的同步置信度有界真发现比例视角

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-04-23 DOI: 10.1016/j.csda.2025.108197

David Swanson

{"title":"A simultaneous confidence-bounded true discovery proportion perspective on localizing differences in smooth terms in regression models","authors":"David Swanson","doi":"10.1016/j.csda.2025.108197","DOIUrl":"10.1016/j.csda.2025.108197","url":null,"abstract":"<div><div>A method is demonstrated for localizing where two spline terms, or smooths, differ using a true discovery proportion (TDP)-based interpretation. The procedure yields a statement on the proportion of some region where true differences exist between two smooths. The methodology avoids ad hoc approaches to making such statements, like subsetting the data and performing hypothesis tests on the truncated spline terms. TDP estimates are 1-<em>α</em> confidence-bounded simultaneously, which means that a region's TDP estimate is a lower bound on the proportion of actual differences, or true discoveries, in that region, with high confidence regardless of the number of estimates made. The procedure is based on closed-testing using Simes local test. This local test requires that the multivariate <span><math><msup><mrow><mi>χ</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> test statistics of generalized Wishart type underlying the method be positive regression dependent on subsets (PRDS), a result for which evidence is presented suggesting that the condition holds. Consistency of the procedure is demonstrated for generalized additive models with the tuning parameter chosen by REML or GCV, and the achievement of confidence-bounded TDP is shown in simulation as is an analysis of walking gait.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"211 ","pages":"Article 108197"},"PeriodicalIF":1.5,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906892","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 : 2025-04-10 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":"2025-04-10","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 : 2025-04-10 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 : 2025-04-04 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":"2025-04-04","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

Multiply robust estimation of causal effects using linked data 使用关联数据乘以因果效应的稳健估计

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-04-02 DOI: 10.1016/j.csda.2025.108175

Shanshan Luo , Yechi Zhang , Wei Li , Zhi Geng

{"title":"Multiply robust estimation of causal effects using linked data","authors":"Shanshan Luo , Yechi Zhang , Wei Li , Zhi Geng","doi":"10.1016/j.csda.2025.108175","DOIUrl":"10.1016/j.csda.2025.108175","url":null,"abstract":"<div><div>Unmeasured confounding presents a common challenge in observational studies, potentially making standard causal parameters unidentifiable without additional assumptions. Given the increasing availability of diverse data sources, exploiting data linkage offers a potential solution to mitigate unmeasured confounding within a primary study of interest. However, this approach often introduces selection bias, as data linkage is feasible only for a subset of the study population. To address such a concern, this paper explores three nonparametric identification strategies assuming that a unit's inclusion in the linked cohort is determined solely by the observed confounders, while acknowledging that the ignorability assumption may depend on some partially unobserved covariates. The existence of multiple identification strategies motivates the development of estimators that effectively capture distinct components of the observed data distribution. Appropriately combining these estimators yields triply robust estimators for the average treatment effect. These estimators remain consistent if at least one of the three distinct parts of the observed data law is correct. Moreover, they are locally efficient if all the models are correctly specified. The proposed estimators are evaluated using simulation studies and real data analysis.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"209 ","pages":"Article 108175"},"PeriodicalIF":1.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769157","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

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 : 2025-03-31 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":"2025-03-31","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

Discretization: Privacy-preserving data publishing for causal discovery 离散化：隐私保护数据发布的因果发现

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-03-27 DOI: 10.1016/j.csda.2025.108174

Youngmin Ahn , Woongjoon Park , Gunwoong Park

{"title":"Discretization: Privacy-preserving data publishing for causal discovery","authors":"Youngmin Ahn , Woongjoon Park , Gunwoong Park","doi":"10.1016/j.csda.2025.108174","DOIUrl":"10.1016/j.csda.2025.108174","url":null,"abstract":"<div><div>As the importance of data privacy continues to grow, data masking has emerged as a crucial method. Notably, data masking techniques aim to protect individual privacy, while enabling data analysts to derive meaningful statistical results, such as the identification of directional or causal relationships between variables. Hence, this study demonstrates the advantages of a quantile-based discretization for protecting privacy and uncovering the relationships between variables in Gaussian directed acyclic graphical (DAG) models. Specifically, it introduces quantile-discretized Gaussian DAG models where each node variable is discretized based on the quantiles. Additionally, it proposes the bi-partition process, which aids in recovering the covariance matrix; hence, the models can be identifiable. Furthermore, a consistent algorithm is developed for learning the underlying structure using the quantile-based discretized data. Finally, through numerical experiments and the application of DAG learning algorithms to discretized MLB data, the proposed algorithm is demonstrated to significantly outperform the state-of-the-art DAG model learning algorithms.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"209 ","pages":"Article 108174"},"PeriodicalIF":1.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738596","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

Efficient regularized estimation of graphical proportional hazards model with interval-censored data 区间截尾数据下图形比例风险模型的有效正则化估计

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-03-27 DOI: 10.1016/j.csda.2025.108178

Huimin Lu , Yilong Wang , Heming Bing , Shuying Wang , Niya Li

引用次数: 0