Journal of Applied Statistics最新文献

Statement of Retraction: A comparative study of computation approaches of the generalized F-test. 撤稿声明：广义f检验计算方法的比较研究。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2026-09-10 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2026.2726692

引用次数: 0

A data-driven single substitution to overcome bias from conventional single substitution for LOD missingness in covariates in least squares regression. 一种数据驱动的单次替换，以克服最小二乘回归中协变量LOD缺失的传统单次替换带来的偏差。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2026-08-27 DOI: 10.1080/02664763.2026.2717994

Ruofei Du, Xinmin Chu, Jing Jin, Li Luo, Ji-Hyun Lee, Laurie G Hudson, Debra MacKenzie, Johnnye Lewis

{"title":"A data-driven single substitution to overcome bias from conventional single substitution for LOD missingness in covariates in least squares regression.","authors":"Ruofei Du, Xinmin Chu, Jing Jin, Li Luo, Ji-Hyun Lee, Laurie G Hudson, Debra MacKenzie, Johnnye Lewis","doi":"10.1080/02664763.2026.2717994","DOIUrl":"10.1080/02664763.2026.2717994","url":null,"abstract":"The Limit of Detection (LOD) is the threshold below which a measurement is considered too low to be reliably quantified. Even though advanced statistical methods exist for dealing with the values below the LOD, simple approaches are still frequently used in real-world settings. Common methods include complete-case analysis, which must use a reduced sample size, and single substitution using fixed constants (e.g. <math><mtext>LOD</mtext> <mo>/</mo> <mo>√</mo> <mn>2</mn></math> ) or other alternatives, which can introduce bias. We propose a data-driven single substitution method for addressing LOD-related missingness in covariates in least squares regression analyses. Instead of relying on constants determined by LODs, our approach computes substitution values based on both unbiased estimates of outcome values associated with the LOD-missing covariates and the linear relationship assumed in the least squares framework. To facilitate hypothesis testing of association effects, we incorporate a bootstrap-based variance estimation procedure. Our simulation studies demonstrate that the proposed method yields unbiased estimates compared to conventional single substitution, achieves lower mean squared errors than the complete-case approach, and provides greater statistical power to detect associations.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13621957/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149080925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

miR-424-5p inhibited malignant behavior of colorectal cancer cells by targeting CCNE1. miR-424-5p 通过靶向 CCNE1 抑制结直肠癌细胞的恶性行为。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2026-06-01 Epub Date: 2019-07-29 DOI: 10.23736/S0031-0808.19.03708-X

Binhua Jiang, Denghai Wu, Lu Huang, Huajin Fang

引用次数: 0

Elastic functional Cox regression model with shape predictors. 具有形状预测因子的弹性函数Cox回归模型。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2026-05-30 DOI: 10.1080/02664763.2026.2680085

Yi Tang Chen, Sebastian Kurtek

{"title":"Elastic functional Cox regression model with shape predictors.","authors":"Yi Tang Chen, Sebastian Kurtek","doi":"10.1080/02664763.2026.2680085","DOIUrl":"10.1080/02664763.2026.2680085","url":null,"abstract":"Phase variability in functional data can obscure prominent geometric features, e.g. local extrema, and deteriorate predictive performance of regression models with functional predictors. We propose an elastic functional Cox regression model (EFCRM) that captures the association between a time-to-event outcome and the shape of a functional predictor. As such, EFCRM accounts for phase variation in the functional predictor via registration, which is supervised by the survival outcome. The model is fit using an iterative algorithm that alternates between removal of phase variation and estimation of the regression coefficient function. To improve convergence of the estimation procedure, we use a small number of separate gradient-based updates at each iteration for the two steps. The model is assessed based on estimation accuracy and predictive performance using a simulation study. We additionally apply the proposed framework to two real-world datasets. Overall, we show that the EFCRM is robust to misspecification of the number and type of basis functions used to approximate the coefficient function and results in comparable or better estimation accuracy and predictive ability than competitors.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13312028/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148351997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Deep learning for the joint analysis of item-level longitudinal and survival data. 用于项目级纵向和生存数据联合分析的深度学习。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2026-02-09 DOI: 10.1080/02664763.2026.2625122

Jeffrey Lin, Sheng Luo

{"title":"Deep learning for the joint analysis of item-level longitudinal and survival data.","authors":"Jeffrey Lin, Sheng Luo","doi":"10.1080/02664763.2026.2625122","DOIUrl":"10.1080/02664763.2026.2625122","url":null,"abstract":"Patients with neurodegenerative diseases are often assessed using rating scales containing a number of items, where each item is scored based on an ordinal scale of 0 to <math><mi>K</mi></math> (0 indicating normal function and <math><mi>K</mi></math> indicating severe impairment). The total score, calculated as the total sum of the items, is commonly used for subsequent analysis due to its simplicity. However, the total score is treated as a continuous value and does not respect the ordinal nature of the item-level data. In addition, the total score may lead to information loss as neurodegenerative diseases are multi-faceted, and using a single numeric value may not effectively represent the disease progression. In this article, we propose a convolutional neural network (CNN) designed to take longitudinal ordinal items as input and predict patients' future survival trajectories. We demonstrate that using the item-level data improves the predictive performance in comparison to traditional joint models using the total score. These advantages are shown through both a simulation study and real data application to a Parkinson's disease study.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13128146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147815603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Estimating Baseline Cutoffs for DHA Dosage in Preterm Birth Prevention: A Bayesian Personalized Change-Point Analysis. 估计DHA剂量在早产预防中的基线截止：贝叶斯个性化变化点分析。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2026-02-06 DOI: 10.1080/02664763.2026.2625118

Jianzheng Wu, Danielle N Christifano, Susan E Carlson, Byron J Gajewski

{"title":"Estimating Baseline Cutoffs for DHA Dosage in Preterm Birth Prevention: A Bayesian Personalized Change-Point Analysis.","authors":"Jianzheng Wu, Danielle N Christifano, Susan E Carlson, Byron J Gajewski","doi":"10.1080/02664763.2026.2625118","DOIUrl":"10.1080/02664763.2026.2625118","url":null,"abstract":"Preterm birth (PTB, <37 weeks gestation) is the leading cause of infant mortality and significant health and socioeconomic burdens that affects millions of newborns and families. While docosahexaenoic acid (DHA) supplementation has shown promise in reducing PTB risk, its effectiveness at reducing the most consequential early PTB (ePTB, <34 weeks gestation) depends on baseline DHA levels, with lower DHA levels and intake linked to a higher risk of PTB and ePTB that can be reduced by high-dose DHA supplementation. Given the higher costs of high-dose DHA, personalized treatment strategies based on baseline DHA levels are needed. We proposed a novel Bayesian personalized change-point model to optimize DHA supplementation strategies based on individual baseline DHA intake. By incorporating Bayesian change-point, dynamic linear, and normal mixture models, our approach estimates optimal DHA baseline thresholds and distribution. We applied this model to real-world data and simulated trials to demonstrate its ability to improve secondary analysis and trial design by adjusting for baseline DHA heterogeneity. This personalized approach can help clinicians identify optimal DHA supplementation doses for individual patients, and it can be applied to other trial studies where the heterogenous characteristics of patients can be quantified.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12959820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147365525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Correction. 修正。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2026-02-04 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2026.2624888

引用次数: 0

Optimal design of acceptance sampling by attributes for sequential tests at consecutive times. 连续次连续试验按属性抽样验收的优化设计。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2026-01-25 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2606240

Hugalf Bernburg, Katy Klauenberg, Stefan Ankirchner

{"title":"Optimal design of acceptance sampling by attributes for sequential tests at consecutive times.","authors":"Hugalf Bernburg, Katy Klauenberg, Stefan Ankirchner","doi":"10.1080/02664763.2025.2606240","DOIUrl":"https://doi.org/10.1080/02664763.2025.2606240","url":null,"abstract":"Naturally, consumers of products want to have the reliability of the products ensured, whereas producers want to minimize the costs of ensuring reliability. For this adversarial setting, we consider group sequential acceptance sampling by attributes that is conducted every few years. We state the associated producer's stopping problem in a simple tree structure. We give conditions under which an optimal sampling design and minimal expected costs can be calculated using the Bellman principle and provide an algorithm to do so. Finally, we illustrate our approach by applying the optimization to the surveillance of utility meters subject to German regulations. For this, we use realistic and easily adaptable classes of cost functions and predict subsequent observations with a Bayesian analysis using prior knowledge. The prior knowledge is gained from historical observations and assuming the devices to follow a lifetime model. This case study indicates that compared to optimal single sampling plans, optimal sequential sampling can decrease the costs of water meter surveillance by almost 30%.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 13","pages":"2497-2519"},"PeriodicalIF":1.1,"publicationDate":"2026-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149110169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Controlling FDR in selecting group-level simultaneous signals from multiple data sources with application to the National COVID Collaborative Cohort data. 控制FDR选择来自多个数据源的组级同步信号，并应用于国家COVID协同队列数据。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2026-01-16 DOI: 10.1080/02664763.2025.2606238

Runqiu Wang, Ran Dai, Hongying Dai, Evan French, Cheng Zheng

{"title":"Controlling FDR in selecting group-level simultaneous signals from multiple data sources with application to the National COVID Collaborative Cohort data.","authors":"Runqiu Wang, Ran Dai, Hongying Dai, Evan French, Cheng Zheng","doi":"10.1080/02664763.2025.2606238","DOIUrl":"10.1080/02664763.2025.2606238","url":null,"abstract":"One challenge in exploratory association studies using observational data is that the associations between the predictors and the outcome are potentially weak and rare, and the candidate predictors have complex correlation structures. False discovery rate (FDR) controlling procedures can provide important statistical guarantees for replicability in predictor identification in exploratory research. In the recently established National COVID Collaborative Cohort (N3C), electronic health record (EHR) data on the same set of grouped candidate predictors are independently collected in multiple different data contributing sites, offering opportunities to identify true associations by combining information from different sources. One challenge is to handle the heterogeneous data types for the same clinical endpoint from the multiple sites. This paper addresses this challenge by presenting a general knockoff-based variable selection algorithm to identify associations from unions of group-level conditional independence tests (simultaneous signals) with exact FDR control guarantees under finite sample settings. This algorithm can work with general regression settings, allowing heterogeneity of both the predictors and the outcomes across multiple data sources. We demonstrate the performance of this method with extensive numerical studies and an application to the N3C data.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13120768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 generalized sigmoidal quantile function approach to smoothed quantile regression. 平滑分位数回归的广义s型分位数函数方法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2026-01-03 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2606966

Alan D Hutson

引用次数: 0