数学最新文献_第5页

A Time-varying Subdistribution Hazard Model with Neural Network 基于神经网络的时变子分布风险模型

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-10-15 DOI: 10.1080/10618600.2026.2748038

Nengjie Zhu,Yue Zhang,Zhangsheng Yu

引用次数: 0

On strongly reversible elements in the Riordan group 在Riordan族的强可逆元素上

IF 1.1 4区数学

Linear & Multilinear Algebra Pub Date : 2026-10-15 DOI: 10.1080/03081087.2026.2748869

Roksana Słowik,Tejbir Lohan

引用次数: 0

Best subset selection meets resampling: tackling selection instability at very low numbers of events per variable. 最佳子集选择满足重新采样：在每个变量的事件数非常低的情况下解决选择不稳定性。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2026-10-15 DOI: 10.1515/ijb-2025-0024

Ricardo Knauer, Nicholas M Brisson, Georg N Duda, Deborah Falla, David W Evans, Erik Rodner

{"title":"Best subset selection meets resampling: tackling selection instability at very low numbers of events per variable.","authors":"Ricardo Knauer, Nicholas M Brisson, Georg N Duda, Deborah Falla, David W Evans, Erik Rodner","doi":"10.1515/ijb-2025-0024","DOIUrl":"https://doi.org/10.1515/ijb-2025-0024","url":null,"abstract":"Trust is a basic necessity for translating machine learning models from bench to bedside. To foster clinicians' trust in predictive models, these models should be interpretable or explainable. A major prerequisite for interpretability or explainability is that feature subsets are stable, i.e., that predictors returned by feature or best subset selection approaches do not change dramatically when the data is perturbed only slightly. However, prior research has shown that stable selections are usually not possible when the number of labels in the smaller class per feature, or outcome events per variable (EPV), is very low, and it remains challenging to build stable, interpretable models in such cases. In this work, we conduct a narrative review in the very low EPV regime and propose to combine cardinality-constrained best subset selection for logistic regression with resampling to tackle the selection instability. We perform a comprehensive evaluation on both synthetic and real-world clinical data, and find that our novel resampling-based model selection procedure yields interpretable feature subsets with a better discriminative performance and stability compared to best subset selection without resampling as well as feature selection with and without resampling. A Python package implementing the proposed methods is available at https://github.com/ml-lab-htw/l0l2learn.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

A new form of Gallai theorem on regular hypergraphs 正则超图上Gallai定理的一个新形式

IF 1 4区数学

Journal of Combinatorial Optimization Pub Date : 2026-10-15 DOI: 10.1007/s10878-026-01486-5

Zhongzheng Tang,Haoyang Zou,Zhuo Diao

引用次数: 0

Estimating heterogeneous treatment effects with survival outcomes via a deep survival learner. 通过深度生存学习者估计异质性治疗效果与生存结果。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-10-15 DOI: 10.1007/s10985-026-09734-w

Yuming Sun, Jian Kang, Yi Li

{"title":"Estimating heterogeneous treatment effects with survival outcomes via a deep survival learner.","authors":"Yuming Sun, Jian Kang, Yi Li","doi":"10.1007/s10985-026-09734-w","DOIUrl":"10.1007/s10985-026-09734-w","url":null,"abstract":"Estimating heterogeneous treatment effects in survival settings is complicated by right censoring as well as the time-varying nature of the estimand. While the conditional average treatment effect (CATE) provides a natural target, most existing approaches focus on a single prespecified time point and do not account for temporal trajectory, leading to instability in estimation. We propose a deep survival learner (DSL) for estimating heterogeneous treatment effects with right-censored outcomes. The method is based on a doubly robust pseudo-outcome whose conditional expectation identifies time-specific CATEs under standard assumptions. This construction remains unbiased if either the outcome model or the treatment assignment model is correctly specified, when properly accounting for censoring. To estimate CATEs over a clinically relevant time spectrum, DSL employs a multi-output deep neural network with shared representations, enabling joint estimation of treatment effect trajectories. From a theoretical perspective, we derive error bounds for both pointwise and joint estimation over time. We show that joint estimation can leverage temporal structure to control estimation error without incurring much additional approximation cost under smoothness conditions, leading to improved stability relative to separate estimation. Cross-fitting is incorporated to reduce overfitting and mitigate bias arising from flexible nuisance estimation. Simulation studies demonstrate favorable finite-sample performance, particularly under nuisance model misspecification. Applied to the Boston Lung Cancer Study, DSL reveals heterogeneity in the effects of perioperative chemotherapy across patient characteristics and over time.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 4","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13672394/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Robustifying Approximate Bayesian Computation 鲁棒化近似贝叶斯计算

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-10-14 DOI: 10.1080/10618600.2026.2748043

Chaya Weerasinghe,Rubén Loaiza-Maya,Christopher Drovandi,David T. Frazier

引用次数: 0

Estimating relapse time distribution from longitudinal biomarker trajectories using iterative regression and continuous time Markov processes. 利用迭代回归和连续时间马尔可夫过程估计纵向生物标志物轨迹的复发时间分布。

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2026-10-14 DOI: 10.1515/ijb-2025-0054

Alice Cleynen, Benoîte de Saporta, Amélie Vernay

{"title":"Estimating relapse time distribution from longitudinal biomarker trajectories using iterative regression and continuous time Markov processes.","authors":"Alice Cleynen, Benoîte de Saporta, Amélie Vernay","doi":"10.1515/ijb-2025-0054","DOIUrl":"https://doi.org/10.1515/ijb-2025-0054","url":null,"abstract":"Biomarker measurements obtained by blood sampling are often used as a non-invasive means of monitoring tumour progression in cancer patients. Diseases evolve dynamically over time, and studying longitudinal observations of specific biomarkers can help to understand patients response to treatment and predict disease progression. We propose a novel iterative regression-based method to estimate changes in patients status within a cohort that includes censored patients, and illustrate it on clinical data from myeloma cases. We formulate the relapse time estimation problem in the framework of Piecewise Deterministic Markov processes (PDMP), where the Euclidean component is a surrogate biomarker for patient state. This approach enables continuous-time estimation of the status-change dates, which in turn allows for accurate inference of the relapse time distribution. A key challenge lies in the partial observability of the process, a complexity that has been rarely addressed in previous studies. We evaluate the performance of our procedure through a simulation study and compare it with different approaches. This work is a proof of concept on biomarker trajectories with simple behaviour, but our method can easily be extended to more complex dynamics.","PeriodicalId":50333,"journal":{"name":"International Journal of Biostatistics","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149185633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Online covariance estimation for stochastic gradient descent under Markovian sampling 马尔可夫抽样下随机梯度下降的在线协方差估计

IF 3.7 1区数学

Journal of the American Statistical Association Pub Date : 2026-10-14 DOI: 10.1080/01621459.2026.2743842

Abhishek Roy,Krishnakumar Balasubramanian

引用次数: 0

DRtool: An Interactive Tool for Analyzing High-Dimensional Clusterings DRtool：一个分析高维聚类的交互式工具

IF 2.4 2区数学

Journal of Computational and Graphical Statistics Pub Date : 2026-10-14 DOI: 10.1080/10618600.2026.2748036

Justin Lin,Julia Fukuyama

引用次数: 0

Dependent Dirichlet processes via thinning 依赖狄利克雷减薄过程