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

High-dimensional response growth curve modeling for longitudinal neuroimaging analysis 纵向神经成像分析的高维反应增长曲线建模

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-07-07 DOI: 10.1016/j.csda.2025.108239

Lu Wang , Xiang Lyu , Lexin Li

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引用次数: 0

Simultaneously detecting spatiotemporal changes with penalized Poisson regression models 用惩罚泊松回归模型同时检测时空变化

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-07-07 DOI: 10.1016/j.csda.2025.108240

Zerui Zhang , Xin Wang , Xin Zhang , Jing Zhang

引用次数: 0

New tests for the identity and sphericity of high-dimensional covariance matrices via U-statistics 用u统计量检验高维协方差矩阵的恒等性和球性

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-07-05 DOI: 10.1016/j.csda.2025.108242

Xiaoge Xiong

引用次数: 0

High-dimensional and banded integer-valued autoregressive processes 高维带整数值自回归过程

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-07-04 DOI: 10.1016/j.csda.2025.108243

Nuo Xu, Kai Yang

引用次数: 0

Conditional inference for ultrahigh-dimensional additive hazards model 超高维加性危险模型的条件推理

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-07-04 DOI: 10.1016/j.csda.2025.108244

Meiling Hao , Ruiyu Yang , Fangfang Bai , Liuquan Sun

{"title":"Conditional inference for ultrahigh-dimensional additive hazards model","authors":"Meiling Hao , Ruiyu Yang , Fangfang Bai , Liuquan Sun","doi":"10.1016/j.csda.2025.108244","DOIUrl":"10.1016/j.csda.2025.108244","url":null,"abstract":"<div><div>In the realm of high-throughput genomic data, modeling with ultrahigh-dimensional covariates and censored survival outcomes is of great importance. We conduct conditional inference for the ultrahigh-dimensional additive hazards model, allowing both the covariates of interest and nuisance covariates to be ultrahigh-dimensional. The presence of right censorship with survival outcomes adds an extra layer of complexity to the original data structure, posing significant challenges for the ultrahigh-dimensional additive hazards model. To address this, we introduce an innovative test statistic based on the quadratic norm of the score function. Moreover, when there is a high correlation between the covariates of interest and nuisance covariates, we propose a decorrelated score function-based test statistic to enhance statistical power. Additionally, we establish the limiting distributions of the test statistics under both the null and local alternative hypotheses, further enhancing the computational appeal of our approach. The proposed statistics are thoroughly evaluated through extensive simulation studies and applied to two real data examples.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"212 ","pages":"Article 108244"},"PeriodicalIF":1.5,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571288","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

Pure interaction effects unseen by Random Forests 随机森林看不到的纯粹互动效果

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-07-01 DOI: 10.1016/j.csda.2025.108237

Ricardo Blum , Munir Hiabu , Enno Mammen , Joseph T. Meyer

引用次数: 0

Variable selection for spatio-temporal conditionally Poisson point processes 时空条件泊松点过程的变量选择

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-06-27 DOI: 10.1016/j.csda.2025.108238

Achmad Choiruddin , Jonatan A. González , Jorge Mateu , Alwan Fadlurohman , Rasmus Waagepetersen

{"title":"Variable selection for spatio-temporal conditionally Poisson point processes","authors":"Achmad Choiruddin , Jonatan A. González , Jorge Mateu , Alwan Fadlurohman , Rasmus Waagepetersen","doi":"10.1016/j.csda.2025.108238","DOIUrl":"10.1016/j.csda.2025.108238","url":null,"abstract":"<div><div>Spatio-temporal point pattern data are becoming prevalent in many scientific disciplines. We consider a sequence of spatial point processes where each point process is Poisson given the past. We model the conditional first-order intensity function of each point process as a parametric log-linear function of spatial, temporal, and spatio-temporal covariates that may depend on previous point patterns. Dealing with spatio-temporal covariates brings computational and methodological challenges compared to the purely spatial case. We extend regularisation methods for spatial point process variable selection to obtain parsimonious and interpretable models in the considered spatio-temporal case. Using our proposed methodology, we conduct two simulation studies and examine an application to criminal activity in the Kennedy district of Bogota. In the application, we consider a spatio-temporal point pattern data of crime locations and a number of spatial, temporal, and spatio-temporal covariates related to urban places, environmental factors, and further space-time factors. The intensity function of vehicle thefts is estimated, considering other crimes as covariate information. The proposed methodology offers a comprehensive approach for analysing spatio-temporal point pattern crime data, capturing complex relationships between covariates and crime occurrences over space and time.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"212 ","pages":"Article 108238"},"PeriodicalIF":1.5,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535764","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 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 : 2025-06-25 DOI: 10.1016/j.csda.2025.108236

Shufang Wei , Yaping Deng , Yaxing Yang

引用次数: 0

Bayesian selection approach for categorical responses via multinomial probit models 基于多项概率模型的分类响应贝叶斯选择方法

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-06-20 DOI: 10.1016/j.csda.2025.108233

Chi-Hsiang Chu , Kuo-Jung Lee , Chien-Chin Hsu , Ray-Bing Chen

引用次数: 0

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 : 2025-06-20 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":"2025-06-20","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