Computational Statistics & Data Analysis最新文献_第5页

Generalized composite multi-sample tests for high-dimensional data 高维数据的广义复合多样本检验

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-02-01 Epub Date: 2025-09-21 DOI: 10.1016/j.csda.2025.108279

Xiaoli Kong , Alejandro Villasante-Tezanos , David W. Fardo , Solomon W. Harrar

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 : 2026-02-01 Epub Date: 2025-08-27 DOI: 10.1016/j.csda.2025.108268

Xiao-Ge Yu, Han-Ying Liang

引用次数: 0

An algorithm for estimating threshold boundary regression models 估计阈值边界回归模型的算法

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-02-01 Epub Date: 2025-09-15 DOI: 10.1016/j.csda.2025.108274

Chih-Hao Chang , Takeshi Emura , Shih-Feng Huang

引用次数: 0

Rate accelerated inference for integrals of multivariate random functions 多元随机函数积分的速率加速推理

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-02-01 Epub Date: 2025-09-11 DOI: 10.1016/j.csda.2025.108273

Valentin Patilea, Sunny G․ W․ Wang

引用次数: 0

Bayesian optimization sequential surrogate (BOSS) algorithm: Fast Bayesian inference for a broad class of Bayesian hierarchical models 贝叶斯优化顺序代理（BOSS）算法：针对广泛的贝叶斯层次模型的快速贝叶斯推理

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-01-01 Epub Date: 2025-07-23 DOI: 10.1016/j.csda.2025.108253

Dayi Li , Ziang Zhang

{"title":"Bayesian optimization sequential surrogate (BOSS) algorithm: Fast Bayesian inference for a broad class of Bayesian hierarchical models","authors":"Dayi Li , Ziang Zhang","doi":"10.1016/j.csda.2025.108253","DOIUrl":"10.1016/j.csda.2025.108253","url":null,"abstract":"<div><div>Approximate Bayesian inference based on Laplace approximation and quadrature has become increasingly popular for its efficiency in fitting latent Gaussian models (LGM). However, many useful models can only be fitted as LGMs if some conditioning parameters are fixed. Such models are termed conditional LGMs, with examples including change-point detection, non-linear regression, and many others. Existing methods for fitting conditional LGMs rely on grid search or sampling-based approaches to explore the posterior density of the conditioning parameters; both require a large number of evaluations of the unnormalized posterior density of the conditioning parameters. Since each evaluation requires fitting a separate LGM, these methods become computationally prohibitive beyond simple scenarios. In this work, the Bayesian Optimization Sequential Surrogate (BOSS) algorithm is introduced, which combines Bayesian optimization with approximate Bayesian inference methods to significantly reduce the computational resources required for fitting conditional LGMs. With orders of magnitude fewer evaluations than those required by the existing methods, BOSS efficiently generates sequential design points that capture the majority of the posterior mass of the conditioning parameters and subsequently yields an accurate surrogate posterior distribution that can be easily normalized. The efficiency, accuracy, and practical utility of BOSS are demonstrated through extensive simulation studies and real-world applications in epidemiology, environmental sciences, and astrophysics.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108253"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144702404","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

Boosting interaction tree stumps for modeling interactions 增强交互树桩以建模交互

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.2025.108247

Michael Lau , Tamara Schikowski , Holger Schwender

{"title":"Boosting interaction tree stumps for modeling interactions","authors":"Michael Lau , Tamara Schikowski , Holger Schwender","doi":"10.1016/j.csda.2025.108247","DOIUrl":"10.1016/j.csda.2025.108247","url":null,"abstract":"<div><div>Incorporating interaction effects is essential for accurately modeling complex underlying relationships in many applications. Often, not only strong predictive performance is desired, but also the interpretability of the resulting model. This need is evident in areas such as epidemiology, in which uncovering the interplay of biological mechanisms is critical for understanding complex diseases. Classical linear models, frequently used for constructing genetic risk scores, fail to capture interaction effects autonomously, while modern machine learning methods such as gradient boosting often produce black-box models that lack interpretability. Existing linear interaction models are largely limited to consider two-way interactions. To address these limitations, a novel statistical learning method, BITS (Boosting Interaction Tree Stumps), is introduced to construct linear models while autonomously detecting and incorporating interaction effects. BITS uses gradient boosting on interaction tree stumps, i.e., decision trees with a single split, where in BITS this split can possibly occur on an interaction term. A branch-and-bound approach is employed in BITS to discard weakly predictive terms. For high-dimensional data, a hybrid search strategy combining greedy and exhaustive approaches is proposed. Regularization techniques are integrated to prevent overfitting and the inclusion of spurious interaction effects. Simulation studies and real data applications demonstrate that BITS produces interpretable models with strong predictive performance. Moreover, in the simulation study, BITS primarily identifies truly influential terms.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108247"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680256","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

GMM estimation of fixed effects partially linear additive SAR model with space-time correlated disturbances 具有时空相关扰动的部分线性可加SAR模型的固定效应GMM估计

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.2025.108252

Bogui Li , Jianbao Chen

{"title":"GMM estimation of fixed effects partially linear additive SAR model with space-time correlated disturbances","authors":"Bogui Li , Jianbao Chen","doi":"10.1016/j.csda.2025.108252","DOIUrl":"10.1016/j.csda.2025.108252","url":null,"abstract":"<div><div>In order to study the ubiquitous space-time panel data in real world, a fixed effects partially linear additive spatial autoregressive (SAR) model with space-time correlated disturbances is proposed. Compared to the linear panel model with space-time correlated disturbances, it can simultaneously capture substantial spatial dependence of response, linearity and nonlinearity between response and regressors, spatial and serial correlations of disturbances, and avoid “curse of dimensionality” of nonparametric regression. By using B-splines to fit additive components and constructing linear and quadratic moment conditions which incorporate information in disturbances, the generalized method of moments (GMM) estimators of unknown parameters and additive components are obtained. Under certain regularity assumptions, it is proved that the GMM estimators are consistent and asymptotically normal. Furthermore, the asymptotically efficient best GMM estimators under normality are derived. Monte Carlo simulation and empirical analysis illustrate that the developed estimation method has good finite sample performance and application prospects.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108252"},"PeriodicalIF":1.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686097","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

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 : 2026-01-01 Epub 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":"2026-01-01","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

Sample-specific cooperative learning integrating heterogeneous radiomics and pathomics data 样本特异性合作学习整合异质放射组学和病理数据

IF 1.5 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-01-01 Epub Date: 2025-07-21 DOI: 10.1016/j.csda.2025.108250

Shih-Ting Huang , Graham A. Colditz , Shu Jiang

引用次数: 0

Random effects misspecification and its consequences for prediction in generalized linear mixed models 广义线性混合模型中的随机效应、错配及其预测后果

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2026-01-01 Epub Date: 2025-07-29 DOI: 10.1016/j.csda.2025.108254

Quan Vu , Francis K.C. Hui , Samuel Muller , A.H. Welsh

{"title":"Random effects misspecification and its consequences for prediction in generalized linear mixed models","authors":"Quan Vu , Francis K.C. Hui , Samuel Muller , A.H. Welsh","doi":"10.1016/j.csda.2025.108254","DOIUrl":"10.1016/j.csda.2025.108254","url":null,"abstract":"<div><div>When fitting generalized linear mixed models, choosing the random effects distribution is an important decision. As random effects are unobserved, misspecification of their distribution is a real possibility. Thus, the consequences of random effects misspecification for point prediction and prediction inference of random effects in generalized linear mixed models need to be investigated. A combination of theory, simulation, and a real application is used to explore the effect of using the common normality assumption for the random effects distribution when the correct specification is a mixture of normal distributions, focusing on the impacts on point prediction, mean squared prediction errors, and prediction intervals. Results show that the level of shrinkage for the predicted random effects can differ greatly under the two random effect distributions, and so is susceptible to misspecification. Also, the unconditional mean squared prediction errors for the random effects are almost always larger under the misspecified normal random effects distribution, while results for the mean squared prediction errors conditional on the random effects are more complicated but remain generally larger under the misspecified distribution (especially when the true random effect is close to the mean of one of the component distributions in the true mixture distribution). Results for prediction intervals indicate that the overall coverage probability is, in contrast, not greatly impacted by misspecification. It is concluded that misspecifying the random effects distribution can affect prediction of random effects, and greater caution is recommended when adopting the normality assumption in generalized linear mixed models.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"213 ","pages":"Article 108254"},"PeriodicalIF":1.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738685","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