Journal of Computational and Graphical Statistics最新文献_第7页

A Stability Framework for Parameter Selection in the Minimum Covariance Determinant Problem 最小协方差行列式问题参数选择的稳定性框架

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2025-04-22 DOI: 10.1080/10618600.2025.2495780

Qiang Heng, Hui Shen, Kenneth Lange

引用次数: 0

Sensitivity Analysis for Binary Outcome Misclassification in Randomization Tests via Integer Programming. 基于整数规划的随机化试验二元结果错分类敏感性分析。

IF 1.8 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2025-04-17 DOI: 10.1080/10618600.2025.2461222

Siyu Heng, Pamela A Shaw

{"title":"Sensitivity Analysis for Binary Outcome Misclassification in Randomization Tests via Integer Programming.","authors":"Siyu Heng, Pamela A Shaw","doi":"10.1080/10618600.2025.2461222","DOIUrl":"https://doi.org/10.1080/10618600.2025.2461222","url":null,"abstract":"Conducting a randomization test is a common method for testing causal null hypotheses in randomized experiments. The popularity of randomization tests is largely because their statistical validity only depends on the randomization design, and no distributional or modeling assumption on the outcome variable is needed. However, randomization tests may still suffer from other sources of bias, among which outcome misclassification is a significant one. We propose a model-free and finite-population sensitivity analysis approach for binary outcome misclassification in randomization tests. A central quantity in our framework is \"warning accuracy,\" defined as the threshold such that a randomization test result based on the measured outcomes may differ from that based on the true outcomes if the outcome measurement accuracy did not surpass that threshold. We show how learning the warning accuracy and related concepts can amplify analyses of randomization tests subject to outcome misclassification without adding additional assumptions. We show that the warning accuracy can be computed efficiently for large data sets by adaptively reformulating a large-scale integer program with respect to the randomization design. We apply the proposed approach to the Prostate Cancer Prevention Trial (PCPT). We also developed an open-source R package for implementation of our approach.","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12377470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144955843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Powerful significance testing for unbalanced clusters. 对不平衡集群进行强大的显著性检验。

IF 1.8 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2025-04-16 DOI: 10.1080/10618600.2025.2469756

Thomas H Keefe, J S Marron

引用次数: 0

ProSpar-GP: Scalable Gaussian Process Modeling with Massive Nonstationary Datasets 大规模非平稳数据集的可伸缩高斯过程建模

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2025-04-09 DOI: 10.1080/10618600.2025.2490264

Kevin Li, Simon Mak

引用次数: 0

Dependence-based fuzzy clustering of functional time series 基于依赖的函数时间序列模糊聚类

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2025-04-09 DOI: 10.1080/10618600.2025.2489537

Ángel López-Oriona, Ying Sun, Han Lin Shang

引用次数: 0

Clustering Time-Evolving Networks Using Temporal Exponential-Family Random Graph Models with Conditional Dyadic Independence and Dynamic Latent Blocks 具有条件并矢独立性和动态潜在块的时间指数族随机图模型聚类时变网络

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2025-03-28 DOI: 10.1080/10618600.2025.2484011

Amal Agarwal, Kevin H. Lee, Lingzhou Xue

引用次数: 0

Extrapolation before imputation reduces bias when imputing censored covariates. 外推前的归因减少偏差时，归因剔除协变量。

IF 1.8 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2025-02-05 DOI: 10.1080/10618600.2024.2444323

Sarah C Lotspeich, Tanya P Garcia

{"title":"Extrapolation before imputation reduces bias when imputing censored covariates.","authors":"Sarah C Lotspeich, Tanya P Garcia","doi":"10.1080/10618600.2024.2444323","DOIUrl":"10.1080/10618600.2024.2444323","url":null,"abstract":"Modeling symptom progression to identify ideal subjects for a Huntington's disease clinical trial is problematic since time to diagnosis, a key covariate, can be heavily censored. Imputation is an appealing strategy that replaces the censored covariate with its conditional mean, but existing methods saw over 200% bias under heavy censoring. Calculating conditional means well requires estimating and then integrating over the survival function of the censored covariate from the censored value to infinity. To estimate the survival function flexibly, existing methods use the semiparametric Cox model with Breslow's estimator, leaving the integrand for the conditional means (the survival function) undefined beyond the observed data. The integral is then estimated up to the largest observed covariate value, and this approximation can cut off the tail of the survival function and lead to severe bias. We combine the semiparametric survival estimator with a parametric extension to approximate the integral up to infinity. In simulations, our proposed extrapolation-before-imputation approach substantially reduces the bias seen with existing imputation methods, sometimes even when the parametric extension was misspecified. We further demonstrate how imputing with corrected conditional means can prioritize subjects for clinical trials. The R code to reproduce results is available in the Supplementary Material.","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12435536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145075381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion. 1位矩阵补全的最大化-最小化高斯-牛顿方法。

IF 1.8 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2025-01-09 DOI: 10.1080/10618600.2024.2428610

Xiaoqian Liu, Xu Han, Eric C Chi, Boaz Nadler

{"title":"A Majorization-Minimization Gauss-Newton Method for 1-Bit Matrix Completion.","authors":"Xiaoqian Liu, Xu Han, Eric C Chi, Boaz Nadler","doi":"10.1080/10618600.2024.2428610","DOIUrl":"https://doi.org/10.1080/10618600.2024.2428610","url":null,"abstract":"In 1-bit matrix completion, the aim is to estimate an underlying low-rank matrix from a partial set of binary observations. We propose a novel method for 1-bit matrix completion called Majorization-Minimization Gauss-Newton (MMGN). Our method is based on the majorization-minimization principle, which converts the original optimization problem into a sequence of standard low-rank matrix completion problems. We solve each of these subproblems by a factorization approach that explicitly enforces the assumed low-rank structure and then apply a Gauss-Newton method. Using simulations and a real data example, we illustrate that in comparison to existing 1-bit matrix completion methods, MMGN outputs comparable if not more accurate estimates. In addition, it is often significantly faster, and less sensitive to the spikiness of the underlying matrix. In comparison with three standard generic optimization approaches that directly minimize the original objective, MMGN also exhibits a clear computational advantage, especially when the fraction of observed entries is small.","PeriodicalId":15422,"journal":{"name":"Journal of Computational and Graphical Statistics","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12327443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144955805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Dimensional Block Diagonal Covariance Structure Detection Using Singular Vectors 利用奇异矢量进行高维块对角线协方差结构检测

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2024-11-05 DOI: 10.1080/10618600.2024.2422985

Jan O. Bauer

引用次数: 0

Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates 高斯图形回归与高维变量的多任务学习

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2024-10-31 DOI: 10.1080/10618600.2024.2421246

Jingfei Zhang, Yi Li

引用次数: 0