Computational Statistics & Data Analysis最新文献

Fast autoregressive model for multivariate dependent outcomes with application to lipidomics analysis for Alzheimer’s disease and APOE-ε4 多变量依赖结果的快速自回归模型及其在阿尔茨海默病和APOE-ε4脂质组学分析中的应用

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-09-29 DOI: 10.1016/j.csda.2025.108280

Hwiyoung Lee , Zhenyao Ye , Chixiang Chen , Peter Kochunov , L. Elliot Hong , Shuo Chen

{"title":"Fast autoregressive model for multivariate dependent outcomes with application to lipidomics analysis for Alzheimer’s disease and APOE-ε4","authors":"Hwiyoung Lee , Zhenyao Ye , Chixiang Chen , Peter Kochunov , L. Elliot Hong , Shuo Chen","doi":"10.1016/j.csda.2025.108280","DOIUrl":"10.1016/j.csda.2025.108280","url":null,"abstract":"<div><div>Association analysis of multivariate omics outcomes is challenging due to the high dimensionality and inter-correlation among outcome variables. In practice, the classic multi-univariate analysis approaches are commonly employed, utilizing linear regression models for each individual outcome followed by adjustments for multiplicity through control of the false discovery rate (FDR) or family-wise error rate (FWER). While straightforward, these multi-univariate methods overlook dependencies between outcome variables. This oversight leads to less accurate statistical inferences, characterized by lower power and an increased false discovery rate, ultimately resulting in reduced replicability across studies. Recently, advanced frequentist and Bayesian methods have been developed to account for these dependencies. However, these methods often pose significant computational challenges for researchers in the field. To bridge this gap, a computationally efficient autoregressive multivariate regression model is proposed that explicitly accounts for the dependence structure among outcome variables. Through extensive simulations, it is demonstrated that the approach provides more accurate multivariate inferences than traditional methods and remains robust even under model misspecification. Additionally, the proposed method is applied to investigate whether the associations between serum lipidomics outcomes and Alzheimer’s disease differentiate in <span><math><mrow><mrow><mi>ε</mi></mrow><mn>4</mn></mrow></math></span> allele carriers and non-carriers of the apolipoprotein E (APOE) gene.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"215 ","pages":"Article 108280"},"PeriodicalIF":1.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270815","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

Bootstrap-based goodness-of-fit test for parametric families of conditional distributions 条件分布参数族的自举拟合优度检验

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-09-27 DOI: 10.1016/j.csda.2025.108289

Gitte Kremling, Gerhard Dikta

引用次数: 0

Resampling NANCOVA: Nonparametric analysis of covariance in small samples 重采样NANCOVA：小样本协方差的非参数分析

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-09-26 DOI: 10.1016/j.csda.2025.108290

Konstantin Emil Thiel , Paavo Sattler , Arne C. Bathke , Georg Zimmermann

{"title":"Resampling NANCOVA: Nonparametric analysis of covariance in small samples","authors":"Konstantin Emil Thiel , Paavo Sattler , Arne C. Bathke , Georg Zimmermann","doi":"10.1016/j.csda.2025.108290","DOIUrl":"10.1016/j.csda.2025.108290","url":null,"abstract":"<div><div>Analysis of covariance is a crucial method for improving precision of statistical tests for factor effects in randomized experiments. However, existing solutions suffer from one or more of the following limitations: (i) they are not suitable for ordinal data (as endpoints or explanatory variables); (ii) they require semiparametric model assumptions; (iii) they are inapplicable to small data scenarios due to often poor type-I error control; or (iv) they provide only approximate testing procedures and (asymptotically) exact test are missing. A resampling approach to the NANCOVA framework is investigated. NANCOVA is a fully nonparametric model based on <em>relative effects</em> that allows for an arbitrary number of covariates and groups, where both outcome variable (endpoint) and covariates can be metric or ordinal. Novel NANCOVA tests and a nonparametric competitor test without covariate adjustment were evaluated in extensive simulations. Unlike approximate tests in the NANCOVA framework, the proposed resampling version showed good performance in small sample scenarios and maintained the nominal type-I error well. Resampling NANCOVA also provided consistently high power: up to 26 % higher than the test without covariate adjustment in a small sample scenario with 4 groups and two covariates. Moreover, it is shown that resampling NANCOVA provides an asymptotically exact testing procedure, which makes it the first one with good finite sample performance in the present NANCOVA framework. In summary, resampling NANCOVA can be considered a viable tool for analysis of covariance overcoming issues (i) - (iv).</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"215 ","pages":"Article 108290"},"PeriodicalIF":1.6,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270814","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

Change-point detection in regression models via the max-EM algorithm 基于max-EM算法的回归模型变点检测

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-09-24 DOI: 10.1016/j.csda.2025.108278

Modibo Diabaté , Grégory Nuel , Olivier Bouaziz

{"title":"Change-point detection in regression models via the max-EM algorithm","authors":"Modibo Diabaté , Grégory Nuel , Olivier Bouaziz","doi":"10.1016/j.csda.2025.108278","DOIUrl":"10.1016/j.csda.2025.108278","url":null,"abstract":"<div><div>The problem of breakpoint detection is considered within a regression modeling framework. A novel method, the max-EM algorithm, is introduced, combining a constrained Hidden Markov Model with the Classification-EM algorithm. This algorithm has linear complexity and provides accurate detection of breakpoints and estimation of parameters. A theoretical result is derived, showing that the likelihood of the data, as a function of the regression parameters and the breakpoints location, increases at each step of the algorithm. Two initialization methods for the breakpoints location are also presented to address local maxima issues. Finally, a statistical test in the one breakpoint situation is developed. Simulation experiments based on linear, logistic, Poisson and Accelerated Failure Time regression models show that the final method that includes the initialization procedure and the max-EM algorithm has a strong performance both in terms of parameters estimation and breakpoints detection. The statistical test is also evaluated and exhibits a correct rejection rate under the null hypothesis and a strong power under various alternatives. Two real dataset are analyzed, the UCI bike sharing and the health disease data, where the interest of the method to detect heterogeneity in the distribution of the data is illustrated.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"215 ","pages":"Article 108278"},"PeriodicalIF":1.6,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270817","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

Fast and efficient causal inference in large-scale data via subsampling and projection calibration 基于子采样和投影校准的大规模数据快速有效的因果推理

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-09-22 DOI: 10.1016/j.csda.2025.108281

Miaomiao Su

{"title":"Fast and efficient causal inference in large-scale data via subsampling and projection calibration","authors":"Miaomiao Su","doi":"10.1016/j.csda.2025.108281","DOIUrl":"10.1016/j.csda.2025.108281","url":null,"abstract":"<div><div>Estimating the average treatment effect in large-scale datasets faces significant computational and storage challenges. Subsampling has emerged as a critical strategy to mitigate these issues. This paper proposes a novel subsampling method that builds on the G-estimation method offering the double robustness property. The proposed method uses a small subset of data to estimate computationally complex nuisance parameters, while leveraging the full dataset for the computationally simple final estimation. To ensure that the resulting estimator remains first-order insensitive to variations in nuisance parameters, a projection approach is introduced to optimize the estimation of the outcome regression function and treatment regression function such that the Neyman orthogonality conditions are satisfied. It is shown that the resulting estimator is asymptotically normal and achieves the same convergence rate as the full data-based estimator when either the treatment or the outcome models is correctly specified. Additionally, when both models are correctly specified, the proposed estimator achieves the same asymptotic variance as the full data-based estimator. The finite sample performance of the proposed method is demonstrated through simulation studies and an application to birth data, comprising over 30 million observations collected over the past eight years. Numerical results indicate that the proposed estimator is nearly as computationally efficient as the uniform subsampling estimator, while achieving similar estimation efficiency to the full data-based G-estimator.</div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"214 ","pages":"Article 108281"},"PeriodicalIF":1.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158570","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

Gamma approximation of stratified truncated exact test (GASTE-test) & application 分层截断精确检验（gaste检验）的伽玛近似及其应用

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-09-21 DOI: 10.1016/j.csda.2025.108277

Alexandre Wendling, Clovis Galiez

{"title":"Gamma approximation of stratified truncated exact test (GASTE-test) & application","authors":"Alexandre Wendling, Clovis Galiez","doi":"10.1016/j.csda.2025.108277","DOIUrl":"10.1016/j.csda.2025.108277","url":null,"abstract":"<div><div>The analysis of binary outcomes and features, such as the effect of vaccination on health, often rely on 2 <span><math><mo>×</mo></math></span> 2 contingency tables. However, confounding factors such as age or gender call for stratified analysis, by creating sub-tables, which is common in bioscience, epidemiological, and social research, as well as in meta-analyses. Traditional methods for testing associations across strata, such as the Cochran-Mantel-Haenszel (CMH) test, struggle with small sample sizes and heterogeneity of effects between strata. Exact tests can address these issues, but are computationally expensive. To address these challenges, the Gamma Approximation of Stratified Truncated Exact (GASTE) test is proposed. It approximates the exact statistic of the combination of p-values with discrete support, leveraging the gamma distribution to approximate the distribution of the test statistic under stratification, providing fast and accurate p-value calculations, even when effects vary between strata. The GASTE method maintains high statistical power and low type I error rates, outperforming traditional methods by offering more sensitive and reliable detection. It is computationally efficient and broadens the applicability of exact tests in research fields with stratified binary data. The GASTE method is demonstrated through two applications: an ecological study of Alpine plant associations and a 1973 case study on admissions at the University of California, Berkeley. The GASTE method offers substantial improvements over traditional approaches. The GASTE method is available as an open-source package at <span><span>https://github.com/AlexandreWen/gaste</span><svg><path></path></svg></span>. A Python package is available on PyPI at <span><span>https://pypi.org/project/gaste-test/</span><svg><path></path></svg></span></div></div>","PeriodicalId":55225,"journal":{"name":"Computational Statistics & Data Analysis","volume":"214 ","pages":"Article 108277"},"PeriodicalIF":1.6,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221243","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

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 : 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

Recursive nonparametric predictive for a discrete regression model 离散回归模型的递归非参数预测

IF 1.6 3区数学

Computational Statistics & Data Analysis Pub Date : 2025-09-16 DOI: 10.1016/j.csda.2025.108275

Lorenzo Cappello , Stephen G. Walker

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 : 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 : 2025-09-11 DOI: 10.1016/j.csda.2025.108273

Valentin Patilea, Sunny G․ W․ Wang

引用次数: 0