Computational Statistics & Data Analysis最新文献_第6页

Estimation of semiparametric probit model based on case-cohort interval-censored failure time data 基于病例队列间隔截尾失效时间数据的半参数概率模型估计

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-01-01 Epub Date: 2025-08-10 DOI: 10.1016/j.csda.2025.108266

Mingyue Du, Ricong Zeng

引用次数: 0

Estimating a smooth covariance for functional data 估计函数数据的平滑协方差

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-01-01 Epub Date: 2025-08-06 DOI: 10.1016/j.csda.2025.108255

Uche Mbaka , James Owen Ramsay , Michelle Carey

引用次数: 0

Inferring the dynamics of quasi-reaction systems via nonlinear local mean-field approximations 用非线性局部平均场近似推断准反应系统的动力学

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-01-01 Epub Date: 2025-07-22 DOI: 10.1016/j.csda.2025.108251

Matteo Framba , Veronica Vinciotti , Ernst C. Wit

{"title":"Inferring the dynamics of quasi-reaction systems via nonlinear local mean-field approximations","authors":"Matteo Framba , Veronica Vinciotti , Ernst C. Wit","doi":"10.1016/j.csda.2025.108251","DOIUrl":"10.1016/j.csda.2025.108251","url":null,"abstract":"<div><div>Parameter estimation of kinetic rates in stochastic quasi-reaction systems can be challenging, particularly when the time gap between consecutive measurements is large. Local linear approximation approaches account for the stochasticity in the system but fail to capture the intrinsically nonlinear nature of the mean dynamics of the process. Moreover, the mean dynamics of a quasi-reaction system can be described by a system of ODEs, which have an explicit solution only for simple unitary systems. An approximate analytical solution is derived for generic quasi-reaction systems via a first-order Taylor approximation of the hazard rate. This allows a nonlinear forward prediction of the future dynamics given the current state of the system. Predictions and corresponding observations are embedded in a nonlinear least-squares approach for parameter estimation. The performance of the algorithm is compared to existing methods via a simulation study. Besides the generality of the approach in the specification of the quasi-reaction system and the gains in computational efficiency, the results show an improvement in the kinetic rate estimation, particularly for data observed at large time intervals. Additionally, the availability of an explicit solution makes the method robust to stiffness, which is often present in biological systems. Application to Rhesus Macaque data illustrates the use of the method in the study of cell differentiation.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108251"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686096","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

Empirical likelihood based Bayesian variable selection 基于经验似然的贝叶斯变量选择

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-01-01 Epub Date: 2025-08-13 DOI: 10.1016/j.csda.2025.108258

Yichen Cheng , Yichuan Zhao

{"title":"Empirical likelihood based Bayesian variable selection","authors":"Yichen Cheng , Yichuan Zhao","doi":"10.1016/j.csda.2025.108258","DOIUrl":"10.1016/j.csda.2025.108258","url":null,"abstract":"<div><div>Empirical likelihood is a popular nonparametric statistical tool that does not require any distributional assumptions. The possibility of conducting variable selection via Bayesian empirical likelihood is studied both theoretically and empirically. Theoretically, it is shown that when the prior distribution satisfies certain mild conditions, the corresponding Bayesian empirical likelihood estimators are posteriorly consistent and variable selection consistent. As special cases, the prior of Bayesian empirical likelihood LASSO and SCAD satisfy such conditions and thus can identify the non-zero elements of the parameters with probability approaching 1. In addition, it is easy to verify that those conditions are met for other widely used priors such as ridge, elastic net and adaptive LASSO. Empirical likelihood depends on a parameter that needs to be obtained by numerically solving a non-linear equation. Thus, there exists no conjugate prior for the posterior distribution, which causes the slow convergence of the MCMC sampling algorithm in some cases. To solve this problem, an approximation distribution is used as the proposal to enhance the acceptance rate and, therefore, facilitate faster computation. The computational results demonstrate quick convergence for the examples used in the paper. Both simulations and real data analyses are performed to illustrate the advantages of the proposed methods.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108258"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893327","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

On Jeffreys's cardioid distribution 杰弗里斯的心脏分布

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-01-01 Epub Date: 2025-07-16 DOI: 10.1016/j.csda.2025.108248

Arthur Pewsey

引用次数: 0

Variable selection in AUC-optimizing classification auc优化分类中的变量选择

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-01-01 Epub Date: 2025-08-05 DOI: 10.1016/j.csda.2025.108256

Hyungwoo Kim , Seung Jun Shin

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 : 2025-12-01 Epub 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":"2025-12-01","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

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 : 2025-12-01 Epub Date: 2025-07-05 DOI: 10.1016/j.csda.2025.108242

Xiaoge Xiong

引用次数: 0

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 : 2025-12-01 Epub Date: 2025-07-07 DOI: 10.1016/j.csda.2025.108239

Lu Wang , Xiang Lyu , Lexin Li

{"title":"High-dimensional response growth curve modeling for longitudinal neuroimaging analysis","authors":"Lu Wang , Xiang Lyu , Lexin Li","doi":"10.1016/j.csda.2025.108239","DOIUrl":"10.1016/j.csda.2025.108239","url":null,"abstract":"<div><div>There is increasing interest in modeling high-dimensional longitudinal outcomes in applications such as developmental neuroimaging research. Growth curve model offers a useful tool to capture both the mean growth pattern across individuals, as well as the dynamic changes of outcomes over time within each individual. However, when the number of outcomes is large, it becomes challenging and often infeasible to tackle the large covariance matrix of the random effects involved in the model. A high-dimensional response growth curve model, with three novel components, is proposed: a low-rank factor model structure that substantially reduces the number of parameters in the large covariance matrix, a re-parameterization formulation coupled with a sparsity penalty that selects important fixed and random effect terms, and a computational trick that turns the inversion of a large matrix into the inversion of a stack of small matrices and thus considerably speeds up the computation. An efficient expectation-maximization-type estimation algorithm is developed, and the competitive performance of the proposed method is demonstrated through both simulations and a longitudinal study of brain structural connectivity in association with human immunodeficiency virus.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"212 ","pages":"Article 108239"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580699","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

Testing the equality of high dimensional distributions 测试高维分布的等式

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-12-01 Epub Date: 2025-07-09 DOI: 10.1016/j.csda.2025.108245

Reza Modarres

引用次数: 0