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

Robust selection of the number of change-points via FDR control 通过FDR控制鲁棒选择改变点的数量

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-09-10 DOI: 10.1016/j.csda.2025.108272

Hui Chen , Chengde Qian , Qin Zhou

引用次数: 0

Measure selection for functional linear model 函数线性模型的测度选择

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-09-08 DOI: 10.1016/j.csda.2025.108270

Su I Iao, Hans-Georg Müller

{"title":"Measure selection for functional linear model","authors":"Su I Iao, Hans-Georg Müller","doi":"10.1016/j.csda.2025.108270","DOIUrl":"10.1016/j.csda.2025.108270","url":null,"abstract":"<div><div>Advancements in modern science have led to an increased prevalence of functional data, which are usually viewed as elements of the space of square-integrable functions <span><math><msup><mi>L</mi><mn>2</mn></msup></math></span>. Core methods in functional data analysis, such as functional principal component analysis, are typically grounded in the Hilbert structure of <span><math><msup><mi>L</mi><mn>2</mn></msup></math></span> and rely on inner products based on integrals with respect to the Lebesgue measure over a fixed domain. A more flexible framework is proposed, where the measure can be arbitrary, allowing natural extensions to unbounded domains and prompting the question of optimal measure choice. Specifically, a novel functional linear model is introduced that incorporates a data-adaptive choice of the measure that defines the space, alongside an enhanced function principal component analysis. Selecting a good measure can improve the model’s predictive performance, especially when the underlying processes are not well-represented when adopting the default Lebesgue measure. Simulations, as well as applications to COVID-19 data and the National Health and Nutrition Examination Survey data, show that the proposed approach consistently outperforms the conventional functional linear model.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"214 ","pages":"Article 108270"},"PeriodicalIF":1.6,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099731","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

Kernel density estimation with a Markov chain Monte Carlo sample 核密度估计与马尔可夫链蒙特卡罗样本

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-09-04 DOI: 10.1016/j.csda.2025.108271

Hang J. Kim , Steven N. MacEachern , Young Min Kim , Yoonsuh Jung

{"title":"Kernel density estimation with a Markov chain Monte Carlo sample","authors":"Hang J. Kim , Steven N. MacEachern , Young Min Kim , Yoonsuh Jung","doi":"10.1016/j.csda.2025.108271","DOIUrl":"10.1016/j.csda.2025.108271","url":null,"abstract":"<div><div>Bayesian inference relies on the posterior distribution, which is often estimated with a Markov chain Monte Carlo sampler. The sampler produces a dependent stream of variates from the limiting distribution of the Markov chain, the posterior distribution. When one wishes to display the estimated posterior density, a natural choice is the histogram. However, abundant literature has shown that the kernel density estimator is more accurate than the histogram in terms of mean integrated squared error for an i.i.d. sample. With this as motivation, a kernel density estimation method is proposed that is appropriate for the dependence in the Markov chain Monte Carlo output. To account for the dependence, the cross-validation criterion is modified to select the bandwidth in standard kernel density estimation approaches. A data-driven adjustment to the biased cross-validation method is suggested with introducing the integrated autocorrelation time of the kernel. The convergence of the modified bandwidth to the optimal bandwidth is shown by adapting theorems from the time series literature. Simulation studies show that the proposed method finds the bandwidth close to the optimal value, while standard methods lead to smaller bandwidths under Markov chain samples and hence to undersmoothed density estimates. A study with real data shows that the proposed method has a considerably smaller integrated mean squared error than standard methods. The R package <span>KDEmcmc</span> to implement the suggested algorithm is available on the Comprehensive R Archive Network.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"214 ","pages":"Article 108271"},"PeriodicalIF":1.6,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049123","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

Overview of normal-reference tests for high-dimensional means with implementation in the R package ‘HDNRA’ 在R包‘ HDNRA ’中实现的高维手段的正常参考测试概述

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-09-03 DOI: 10.1016/j.csda.2025.108269

Pengfei Wang , Tianming Zhu , Jin-Ting Zhang

{"title":"Overview of normal-reference tests for high-dimensional means with implementation in the R package ‘HDNRA’","authors":"Pengfei Wang , Tianming Zhu , Jin-Ting Zhang","doi":"10.1016/j.csda.2025.108269","DOIUrl":"10.1016/j.csda.2025.108269","url":null,"abstract":"<div><div>The challenge of testing for equal mean vectors in high-dimensional data poses significant difficulties in statistical inference. Much of the existing literature introduces methods that often rely on stringent regularity conditions for the underlying covariance matrices, enabling asymptotic normality of test statistics. However, this can lead to complications in controlling test size. To address these issues, a new set of tests has emerged, leveraging the normal-reference approach to improve reliability. The latest normal-reference methods for testing equality of mean vectors in high-dimensional samples, potentially with differing covariance structures, are reviewed. The theoretical underpinnings of these tests are revisited, providing a new unified justification for the validity of centralized <span><math><msup><mi>L</mi><mn>2</mn></msup></math></span>-norm-based normal-reference tests (NRTs) by deriving the convergence rate of the distance between the null distribution of the test statistic and its corresponding normal-reference distribution. To facilitate practical application, an <span>R</span> package, <span>HDNRA</span>, is introduced, implementing these NRTs and extending beyond the two-sample problem to accommodate general linear hypothesis testing (GLHT). The package, designed with user-friendliness in mind, achieves efficient computation through a core implemented in <span>C++</span> using <span>Rcpp</span>, <span>OpenMP</span>, and <span>RcppArmadillo</span>. Examples with real datasets are included, showcasing the application of various tests and providing insights into their practical utility.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"214 ","pages":"Article 108269"},"PeriodicalIF":1.6,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010731","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

High-dimensional subgroup functional quantile regression with panel and dependent data 高维亚群功能分位数回归与面板和相关数据

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-08-27 DOI: 10.1016/j.csda.2025.108268

Xiao-Ge Yu, Han-Ying Liang

引用次数: 0

Discovering causal structures in corrupted data: frugality in anchored Gaussian DAG models 发现损坏数据中的因果结构：锚定高斯DAG模型中的节俭性

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-08-18 DOI: 10.1016/j.csda.2025.108267

Joonho Shin , Junhyoung Chung , Seyong Hwang , Gunwoong Park

{"title":"Discovering causal structures in corrupted data: frugality in anchored Gaussian DAG models","authors":"Joonho Shin , Junhyoung Chung , Seyong Hwang , Gunwoong Park","doi":"10.1016/j.csda.2025.108267","DOIUrl":"10.1016/j.csda.2025.108267","url":null,"abstract":"<div><div>This study focuses on the recovery of anchored Gaussian directed acyclic graphical (DAG) models to address the challenge of discovering causal or directed relationships among variables in datasets that are either intentionally masked or contaminated due to measurement errors. A main contribution is to relax the existing restrictive identifiability conditions for anchored Gaussian DAG models by introducing the anchored-frugality assumption. This assumption posits that the true graph is the most frugal among those satisfying the possible distributions of the latent and observed variables, thereby making the true Markov equivalent class (MEC) identifiable. The validity of the anchored-frugality assumption is justified using both graph and probability theories, respectively. Another main contribution is the development of the anchored-SP and frugal-PC algorithms. Specifically, the anchored-SP algorithm finds the most frugal graph among all possible graphs satisfying the Markov condition while the frugal-PC algorithm finds the most frugal graph among some graphs. Hence, the frugal-PC algorithm is more computationally feasible, while it requires an additional frugality-faithfulness assumption for soundness. Various simulations support the theoretical findings of this study and demonstrate the practical effectiveness of the proposed algorithm against state-of-the-art algorithms such as ACI, PC, and MMHC. Furthermore, the applications of the proposed algorithm to protein signaling data and breast cancer data illustrate its effectiveness in uncovering relationships among proteins and among cancer-related cell nuclei characteristics.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108267"},"PeriodicalIF":1.6,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908977","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 : 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":"2025-08-13","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

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 : 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 : 2025-08-06 DOI: 10.1016/j.csda.2025.108255

Uche Mbaka , James Owen Ramsay , Michelle Carey

引用次数: 0

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