{"title":"Statement of Retraction: A comparative study of computation approaches of the generalized F-test.","authors":"","doi":"10.1080/02664763.2026.2726692","DOIUrl":"https://doi.org/10.1080/02664763.2026.2726692","url":null,"abstract":"<p><p>[This retracts the article DOI: 10.1080/02664763.2021.1939660.].</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 13","pages":"2703"},"PeriodicalIF":1.1,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13637756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149110210","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}
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":"<p><p>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.</p>","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}
{"title":"miR-424-5p inhibited malignant behavior of colorectal cancer cells by targeting CCNE1.","authors":"Binhua Jiang, Denghai Wu, Lu Huang, Huajin Fang","doi":"10.23736/S0031-0808.19.03708-X","DOIUrl":"10.23736/S0031-0808.19.03708-X","url":null,"abstract":"","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"33 1","pages":"92-93"},"PeriodicalIF":1.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81412463","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}
{"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":"<p><p>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.</p>","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}
{"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":"<p><p>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.</p>","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}
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":"<p><p>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.</p>","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}
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":"<p><p>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%.</p>","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}
{"title":"Study of the effect of randomized response techniques on the Jarque and Bera test: real survey application.","authors":"Srikar Govardhana, Sarjinder Singh","doi":"10.1080/02664763.2025.2612553","DOIUrl":"10.1080/02664763.2025.2612553","url":null,"abstract":"<p><p>Expressions for the coefficients of skewness and kurtosis of the estimator of the population proportion of a sensitive characteristic under Warner's model are developed in this paper. It is a well-established fact that the variance of Warner's estimator exceeds that of the direct questioning method. In contrast, the coefficients of skewness and kurtosis are shown to decrease. Simulated values of these coefficients are compared with their corresponding theoretical values. The effect of Warner's randomized response technique on the adjusted Jarque-Bera (AJB) test of normality is examined in comparison with estimates based on direct questioning. The likelihood ratio test and the binomial test are also included in the analysis. The findings reveal the surprising result that an estimate of proportion obtained through direct questioning may not follow a normal distribution, whereas the corresponding estimate derived from a randomized response technique may exhibit approximate normality. Assumptions of normality commonly adopted by researchers for randomized response estimators of proportion are also discussed. The results are further supported by an application to real data.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 14","pages":"2912-2930"},"PeriodicalIF":1.1,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13726343/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149321513","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}
{"title":"A marginalized three-part interrupted time series regression model for proportional data.","authors":"Shangyuan Ye, Maricela Cruz, Ziyou Wang, Yun Yu","doi":"10.1080/02664763.2026.2614752","DOIUrl":"10.1080/02664763.2026.2614752","url":null,"abstract":"<p><p>Interrupted time series (ITS) is often used to evaluate the effectiveness of a health policy intervention that accounts for the temporal dependence of outcomes. When the outcome of interest is a percentage or percentile, the data can be highly skewed, bounded in <math><mo>[</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>]</mo></math> , and have many zeros or ones. A three-part Beta regression model is commonly used to separate zeros, ones, and positive values explicitly by three submodels. However, incorporating temporal dependence into the three-part Beta regression model is challenging. In this article, we propose a marginalized zero-one-inflated Beta time series model that captures the temporal dependence of outcomes through a copula and allows investigators to examine covariate effects on the marginal mean. We investigate its practical performance using simulation studies and apply the model to a real ITS study.</p>","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 14","pages":"2882-2911"},"PeriodicalIF":1.1,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13726346/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149321506","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}