Journal of Applied Statistics最新文献_第3页

Predictive modeling of corticosteroids sensitivity in sepsis using a supervised learning approach. 使用监督学习方法对脓毒症中皮质类固醇敏感性的预测建模。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-12-03 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2593309

Elisa Lannelongue, Agnès Grimaud, Charles Tillier, Djillali Annane

{"title":"Predictive modeling of corticosteroids sensitivity in sepsis using a supervised learning approach.","authors":"Elisa Lannelongue, Agnès Grimaud, Charles Tillier, Djillali Annane","doi":"10.1080/02664763.2025.2593309","DOIUrl":"10.1080/02664763.2025.2593309","url":null,"abstract":"Dealing with Sepsis poses a critical challenge in healthcare and necessitates rapid and well-adapted treatment responses. Corticosteroids have been used as a treatment but individual-level effects vary widely. This study aims at improving treatment efficacy by leveraging machine learning techniques to predict patients' sensitivity to corticosteroids. We use two comprehensive datasets of sepsis patients to evaluate two distinct model configurations. These configurations employ Logistic Regression and Random Forest algorithms, both with and without class balancing using the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTE-NC) data augmentation to address mixed data types. Our findings consistently demonstrate that Random Forest models, particularly when paired with appropriate class balancing techniques, outperform other model configurations in predicting corticosteroid sensitivity using both datasets individually and combined. Notably, incorporating SMOTE-NC significantly enhances model performance, underscoring the importance of appropriately addressing imbalanced datasets in predictive modeling.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 11","pages":"2251-2270"},"PeriodicalIF":1.1,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13537058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148880664","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

Multinomial mixture for spatial data. 空间数据的多项混合。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-12-02 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2594624

Anna Nalpantidi, Dimitris Karlis, Panagiotis Papastamoulis

{"title":"Multinomial mixture for spatial data.","authors":"Anna Nalpantidi, Dimitris Karlis, Panagiotis Papastamoulis","doi":"10.1080/02664763.2025.2594624","DOIUrl":"10.1080/02664763.2025.2594624","url":null,"abstract":"The purpose of this paper is to extend standard finite mixture models in the context of multinomial mixtures for spatial data, in order to cluster geographical units according to demographic characteristics. The spatial information is incorporated into the model through the mixing probabilities of each component. To be more specific, a Gibbs distribution is assumed for prior probabilities. In this way, assignment of each observation is affected by neighbors' cluster and spatial dependence is included in the model. Estimation is based on a modified EM algorithm which is enriched by an extra, initial step for approximating the field. The simulated field algorithm is used in this initial step. Simulation studies are also provided to examine the ability of the methodology to properly cluster the data and reveal the true parameters, while focus is given in the performance of the approximated BIC on recovering the true number of clusters. The presented model will be used for clustering municipalities of Attica with respect to age distribution of residents. The results of the analysis have revealed eight distinct clusters. Each cluster includes municipalities with similar age structure of the population, as for example group of municipalities with excess of young adult people because of the universities in these regions.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 12","pages":"2398-2421"},"PeriodicalIF":1.1,"publicationDate":"2025-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13552337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912274","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

Adaptive elastic net variable selection of spatial panel quantile autoregressive model with fixed effects. 具有固定效应的空间面板分位数自回归模型的自适应弹性网变量选择。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-12-01 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2594622

Zhuoxi Yu, Haiyun Liu, Zhiqiang Hua, Xue Yao

引用次数: 0

Robust diagnostic detection of the horizontal pleiotropy on correlated variants in summary data Mendelian randomization. 综合数据中相关变异水平多效性的可靠诊断检测孟德尔随机化。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-27 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2593314

Li-Chu Chien

{"title":"Robust diagnostic detection of the horizontal pleiotropy on correlated variants in summary data Mendelian randomization.","authors":"Li-Chu Chien","doi":"10.1080/02664763.2025.2593314","DOIUrl":"10.1080/02664763.2025.2593314","url":null,"abstract":"Mendelian randomization (MR) is an epidemiological tool that is used to infer the causal relationship between an exposure and an outcome through genetic variants as instrumental variables (IVs) for the exposure. The horizontal pleiotropy can cause biased inference in MR. We conduct a mixture of the pleiotropic variant detection method, called the Mixture of Horizontal Pleiotropy Detection (MPD), for considering the horizontal pleiotropy on correlated variants in two-sample summary data. The MPD uses the inverse variance weighted (IVW) regression without an intercept to identify the invalid IVs from a group of the IVs with directional pleiotropy, while using the Egger regression with an intercept to identify the invalid IVs from a group of the IVs with balanced pleiotropy. The MPD is consequently more detailed for searching for the correlated variants associated with the outcome conditionals on the exposure, in comparison with the current pleiotropic variant detection methods using the IVW- or Egger-based regression without or with an intercept in MR. We use the simulated data and analyze the real-world data with the exposure, low-density lipoprotein cholesterol, on the outcome, coronary artery disease, to investigate the finite-sample properties of the MPD on the pleiotropic variant detection.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 12","pages":"2271-2292"},"PeriodicalIF":1.1,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13552321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912242","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

Using subject-level variability to predict time-varying outcomes: investigating the association between hormone variability and BMD trajectories over the menopausal transition. 使用受试者水平的可变性来预测时间变化的结果：调查激素可变性与绝经过渡期骨密度轨迹之间的关系。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-26 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2589991

Irena Chen, Zhenke Wu, Sioban D Harlow, Carrie A Karvonen-Gutierrez, Michelle M Hood, Michael R Elliott

{"title":"Using subject-level variability to predict time-varying outcomes: investigating the association between hormone variability and BMD trajectories over the menopausal transition.","authors":"Irena Chen, Zhenke Wu, Sioban D Harlow, Carrie A Karvonen-Gutierrez, Michelle M Hood, Michael R Elliott","doi":"10.1080/02664763.2025.2589991","DOIUrl":"10.1080/02664763.2025.2589991","url":null,"abstract":"Women are at increased risk of bone loss during the menopausal transition; in fact, nearly 50% of women's lifetime bone loss occurs during this time. In addition to level and rate of change in estradiol (E2) and follicle-stimulating hormone (FSH), the variability of these hormones may be a key predictor of bone loss; however, this remains unexplored in the existing literature. We introduce a joint model that explicitly characterizes the uncertainty in the means and variances of the hormone trajectories. Our method estimates both the individual mean marker trajectories and the individual residual variances, and links these variances to bone trajectories. In our application, for the first time, higher FSH variance interacted with time was associated with declines in bone mineral density (BMD) across the menopausal transition. At the final menstrual period, higher individual FSH variance predicted an average 0.26% decline in BMD, but this effect is moderated over time. Our results suggest that the mean and variance of FSH, rather than E2, may be a stronger predictor of menopausal bone health. In a variety of simulation studies, our method achieves <math><mrow><mo>></mo></mrow> <mn>90</mn> <mi>%</mi></math> interval coverage, whereas naive two-stage alternatives often fail to propagate uncertainty in the individual-level variance estimates.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 11","pages":"2137-2161"},"PeriodicalIF":1.1,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13537070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148880594","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

Bayesian spatial and spatio-temporal analysis of socioeconomic determinants on COVID-19 mortality. 影响COVID-19死亡率的社会经济因素贝叶斯时空分析

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-26 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2593322

R Muzaffer Musal, Tevfik Aktekin, Tahir Ekin

引用次数: 0

Density-based clustering method with adaptive neighbors. 基于密度的自适应邻居聚类方法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-25 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2593320

Na Lu, Luhong Diao, Yiyi Xing

引用次数: 0

Population-average and subject-specific approaches for the analysis of misclassified correlated binary outcomes with internal validation. 人口平均和主题特定的方法，用于分析错误分类相关二进制结果与内部验证。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-25 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2593319

Hung-Mo Lin, Li Tang, Robert H Lyles, John M Williamson

{"title":"Population-average and subject-specific approaches for the analysis of misclassified correlated binary outcomes with internal validation.","authors":"Hung-Mo Lin, Li Tang, Robert H Lyles, John M Williamson","doi":"10.1080/02664763.2025.2593319","DOIUrl":"10.1080/02664763.2025.2593319","url":null,"abstract":"Misclassification of correlated binary responses may occur in clinical and epidemiological studies, resulting in biased and/or inefficient parameter estimation. We extend existing generalized estimating equation (GEE) approaches to allow for differential misclassification via two sets of estimating equations for the analysis of error-prone correlated binary outcomes when internal validation data are available. One set of estimating equations uses logistic regression to model the misclassification process with the internal validation data, using subject characteristics to estimate the differential sensitivities and specificities of the error-prone response in relation to the gold standard. The second set of estimating equations models the mismeasured binary response by leveraging the subject-specific estimates of sensitivity and specificity. The subject-specific covariates need not be identical in the two sets of estimating equations. We present analysis of longitudinal assessments of bacterial vaginosis from the HIV Epidemiology Research Study (HERS), compare the proposed population-average approach based on GEE with a subject-specific one based on a full-likelihood mixed-effects analysis, and discuss the differing parameter interpretations. We also present results from simulated data with a misclassified binary outcome analyzed with this GEE approach.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 12","pages":"2293-2309"},"PeriodicalIF":1.1,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13552332/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912302","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

Where is the Squad? Robust ideal point estimation in the presence of protest votes. 救援队在哪里？存在抗议投票的鲁棒理想点估计。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-24 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2585949

Kwangok Seo, Joungyoun Kim, Johan Lim, Jong Hee Park

{"title":"Where is the Squad? Robust ideal point estimation in the presence of protest votes.","authors":"Kwangok Seo, Joungyoun Kim, Johan Lim, Jong Hee Park","doi":"10.1080/02664763.2025.2585949","DOIUrl":"10.1080/02664763.2025.2585949","url":null,"abstract":"Ideal point estimation is a widely used statistical method for understanding the preferences of elected representatives in political science, statistics, and social sciences. However, protest votes - where individuals deliberately obscure their true ideal points to express dissatisfaction with their own political party - present a significant challenge to the accuracy of this method. In this paper, we first examine the impact of protest votes on ideal point estimation, demonstrating that they introduce substantial attenuation bias that leads to the misrepresentation of extreme legislators as moderates. After establishing the importance of this issue, we propose a novel method that corrects the bias stemming from protest votes, thereby allowing researchers to obtain more accurate estimates of legislators' ideal points. Our method detects and masks votes suspected to be protest votes within a Bayesian framework, reducing the bias introduced by such votes in posterior inference of ideal points. We demonstrate the effectiveness of our proposed method in addressing the attenuation problem caused by protest votes using both simulated scenarios and real-world roll-call data.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 10","pages":"2012-2027"},"PeriodicalIF":1.1,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13421119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148630562","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

Classification of multivariate functional data with an application to ADHD fMRI data. 多变量功能数据的分类与ADHD fMRI数据的应用。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-11-23 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2567979

Yeji Seong, Iris Ivy Gauran, Hyunsung Kim, Hernando Ombao, Junyong Park

{"title":"Classification of multivariate functional data with an application to ADHD fMRI data.","authors":"Yeji Seong, Iris Ivy Gauran, Hyunsung Kim, Hernando Ombao, Junyong Park","doi":"10.1080/02664763.2025.2567979","DOIUrl":"https://doi.org/10.1080/02664763.2025.2567979","url":null,"abstract":"The classification of resting-state functional magnetic resonance imaging (rs-fMRI) data presents unique challenges in the detection and diagnosis of neuropsychiatric disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD). Traditional classification approaches often prove inadequate when handling complex spatiotemporal patterns and high-dimensional fMRI data, particularly when significant variations exist both between and within diagnostic groups. To address these limitations, we introduce a novel classification framework that integrates three complementary analytical components: elastic registration for curve alignment, geometric curve length computation for capturing signal variability, and sparse principal component analysis for dimensionality reduction. Extensive simulation studies show that our proposed method significantly outperforms existing approaches, especially in scenarios where groups exhibit distinct variation patterns rather than mean differences in their functional curves. When applied to the ADHD-200 dataset, our method achieves classification accuracy rates substantially exceeding conventional approaches. The proposed framework's ability to capture subtle variability differences while maintaining computational efficiency makes it particularly valuable for biomarker discovery and clinical applications in neuropsychiatric research. Our approach's focus on signal variability rather than mean activation patterns offers new insights into the dynamic nature of brain activity differences in ADHD and provides a promising foundation for analyzing other neurological conditions.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 8","pages":"1515-1537"},"PeriodicalIF":1.1,"publicationDate":"2025-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13248495/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148217769","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