Journal of Applied Statistics最新文献_第9页

Addressing outliers in mixed-effects logistic regression: a more robust modeling approach. 处理混合效应逻辑回归中的异常值：一种更稳健的建模方法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-07-25 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2538076

Divan A Burger, Sean van der Merwe, Emmanuel Lesaffre

{"title":"Addressing outliers in mixed-effects logistic regression: a more robust modeling approach.","authors":"Divan A Burger, Sean van der Merwe, Emmanuel Lesaffre","doi":"10.1080/02664763.2025.2538076","DOIUrl":"10.1080/02664763.2025.2538076","url":null,"abstract":"This study introduces an outlier-robust model for analyzing hierarchically structured bounded count data within a Bayesian framework, utilizing a logistic regression approach implemented in JAGS. Our model incorporates a t-distributed latent variable to address overdispersion and outliers, improving robustness compared to conventional models such as the beta-binomial, binomial-logit-normal, and standard binomial models. Notably, our model targets a pseudo-median that differs from the true discrete median by less than one count; this closed-form quantity provides a robust and interpretable measure of central tendency. For comparability between all models, we additionally make predictions based on the mean proportion; however, this involves an integration step for the t-distributed nuisance parameter. While limited literature specifically addresses outliers in mixed models for bounded count data, this research fills that gap. The practical utility of the model is demonstrated using a longitudinal medication adherence dataset, where patient behavior often results in abrupt changes and outliers within individual trajectories. A simulation study demonstrates the binomial-logit-t model's strong performance, with comparison statistics favoring it among the four evaluated models. An additional data contamination simulation confirms its robustness against outliers. Our robust approach maintains the integrity of the dataset, effectively handling outliers to provide more accurate and reliable parameter estimates.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 5","pages":"832-854"},"PeriodicalIF":1.1,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045181/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623031","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 mixed Bell regression model for overdispersed medical count data. 过度分散医疗计数数据的混合贝尔回归模型。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-07-24 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2538084

Naiara C A Dos Santos, Jorge L Bazán, Artur J Lemonte

{"title":"A mixed Bell regression model for overdispersed medical count data.","authors":"Naiara C A Dos Santos, Jorge L Bazán, Artur J Lemonte","doi":"10.1080/02664763.2025.2538084","DOIUrl":"10.1080/02664763.2025.2538084","url":null,"abstract":"In this article, we consider the discrete Bell distribution to introduce a new mixed-effects regression model that may be an interesting alternative to traditional mixed-effects models for count response variables. The new regression model can be applied in several areas including health data. We consider the frequentist and Bayesian approaches to perform inferences in this class of mixed regression models. We provide Monte Carlo simulation experiments to verify the performance of these approaches in estimating the mixed Bell regression model parameters. The simulation results are quite promising and indicate that these approaches are effective in doing that. We also consider model comparison criteria based on the frequentist and Bayesian approaches and simulations are considered to verify the performance of these criteria. Two empirical applications to real data of the proposed mixed-effects model are provided, and comparisons with the Poisson mixed-effects model, as well as the Poisson inverse Gaussian mixed-effects model, are made. The real data applications confirm that the proposed mixed-effects Bell regression model can be an interesting alternative in the modeling of count response variables.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 5","pages":"855-873"},"PeriodicalIF":1.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13045179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147623028","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

Optimal group sizes for testing group mean differences using the Bayes factor. 使用贝叶斯因子检验组均值差异的最优组大小。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-07-24 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2534898

Mirjam Moerbeek

{"title":"Optimal group sizes for testing group mean differences using the Bayes factor.","authors":"Mirjam Moerbeek","doi":"10.1080/02664763.2025.2534898","DOIUrl":"10.1080/02664763.2025.2534898","url":null,"abstract":"Determination of group sizes is an important issue when planning a study that aims to compare mean outcomes across groups. Using equal group sizes is not the best choice in the case of heterogeneous costs and/or variances. Conventional optimal design methodology has shown that groups with higher variance and lower costs should include more subjects. However, these results are based on the framework of null hypothesis significance testing, which has received severe criticism over the past decades. The Bayesian approach to hypothesis testing has been proposed as an alternative and uses the Bayes factor to quantify the support of a hypothesis given the data. Group sizes that maximize the Bayes factor are determined, and it is shown how these optimal group sizes depend on the variances, costs and group means. Furthermore, it is shown to what degree the Bayes factor becomes smaller while using conventional optimal design methodology or equal group sizes. The optimal design methodology is illustrated using examples on multidisciplinary pain management and psychological status and asthma outcomes. A Shiny app has been made available to facilitate the use of the optimal design methodology.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 4","pages":"710-728"},"PeriodicalIF":1.1,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12981267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147468089","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

Assessing the impact of neighborhood structures in Bayesian disease mapping. 评估邻域结构在贝叶斯疾病制图中的影响。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-07-21 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2533479

Minh Hanh Nguyen, Thomas Neyens, Andrew B Lawson, Christel Faes

{"title":"Assessing the impact of neighborhood structures in Bayesian disease mapping.","authors":"Minh Hanh Nguyen, Thomas Neyens, Andrew B Lawson, Christel Faes","doi":"10.1080/02664763.2025.2533479","DOIUrl":"10.1080/02664763.2025.2533479","url":null,"abstract":"In Bayesian disease mapping, defining the neighborhood structure is crucial when fitting the conditional auto-regressive model. Yet, there has been little assessment of how different structures affect the model performance in case of fine-scale data. This paper explores this gap. In a case study examining COVID-19 pandemic effects, 2020 mortality is contrasted with pre-pandemic rates in small areas in Limburg (Belgium). Data are modeled using BYM and BYM2, with three broadening queen-neighborhood structures up to the fifth-order neighbors and two weight schemes. A simulation study assesses model performance in reproducing the pairwise spatial correlation at different neighbor orders. Models are compared regarding WAIC, goodness-of-fit, parameter estimates, and computation time. Results show that the order-based weight matrix performs better than the binary matrix. The simple first-order neighborhood structure shows comparable performance to larger higher-order structures while requiring much less computation time. The BYM model is more impacted by the choice of the neighborhood as compared to the BYM2 model. Our findings suggest minimal advantages in employing higher-order neighborhood matrices. In conclusion, our study indicates that opting for a simple first-order neighborhood structure is a pragmatic and suitable choice when applying a conditional auto-regressive model to fine-scale data in Bayesian disease mapping.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 3","pages":"537-553"},"PeriodicalIF":1.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12954808/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354953","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 biomarker effect estimate for combining data from multiple biomarker studies. 结合多个生物标志物研究数据的贝叶斯生物标志物效应估计。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-07-20 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2528362

Zhiwei Rong, Jiali Song, Fengyu Sun, Chenbo Zhang, Lan Mi, Yuqin Song, Yan Hou

{"title":"Bayesian biomarker effect estimate for combining data from multiple biomarker studies.","authors":"Zhiwei Rong, Jiali Song, Fengyu Sun, Chenbo Zhang, Lan Mi, Yuqin Song, Yan Hou","doi":"10.1080/02664763.2025.2528362","DOIUrl":"10.1080/02664763.2025.2528362","url":null,"abstract":"Pooling data from multiple studies enhances statistical power and precision for quantifying biomarker-disease associations. However, inter-study variability in biomarker measurements exists, requiring calibration to a reference assay to standardize biomarker data across contributing studies before pooling. In this study, we develop a novel Bayesian Biomarker Pooling (BBP) method to aggregate biomarker data from multiple study sources, which considers the reference measurements of biospecimens that have not been re-assayed as unobservable latent variables. We establish a two-level model of studies and biospecimens to delineate the relationships among reference measurements, local measurements, and outcomes. Furthermore, we compare the proposed BBP method with several prevalent methodologies: the internalized method, the full calibration method, the two-stage method, the naïve method, and the x-only method. Our results demonstrate that the BBP method outperforms the other methods. This advantage is particularly pronounced in scenarios involving high noise and strong effects. As an illustrative example, we apply these methods in a pooling analysis to evaluate the association between Human Epidermal Growth Factor Receptor 2 (HER2) gene expression levels and breast cancer risk. The full package is available online at https://github.com/luyiyun/bayesian_biomark_pooling.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 4","pages":"614-632"},"PeriodicalIF":1.1,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12981262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147468167","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

Ridge-penalized Zero-Inflated Probit Bell model for multicollinearity in count data. 计数数据多重共线性的脊罚零膨胀概率Bell模型。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-07-18 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2530551

Essoham Ali, Adewale F Lukman

引用次数: 0

Efficient spline orthogonal basis for representation of density functions. 密度函数表示的有效样条正交基。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-07-17 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2532621

Jana Burkotová, Ivana Pavlů, Hiba Nassar, Jitka Machalová, Karel Hron

{"title":"Efficient spline orthogonal basis for representation of density functions.","authors":"Jana Burkotová, Ivana Pavlů, Hiba Nassar, Jitka Machalová, Karel Hron","doi":"10.1080/02664763.2025.2532621","DOIUrl":"10.1080/02664763.2025.2532621","url":null,"abstract":"Probability density functions form a specific class of functional data objects with intrinsic properties of scale invariance and relative scale characterized by the unit integral constraint. The Bayes spaces methodology respects their specific nature, and the centred log-ratio transformation enables processing such functional data in the standard Lebesgue space of square-integrable functions. As the data representing densities are frequently observed in their discrete form, the focus has been on their spline representation. Therefore, the crucial step in the approximation is to construct a proper spline basis reflecting their specific properties. Since the centred log-ratio transformation forms a subspace of functions with a zero integral constraint, the standard B-spline basis is no longer suitable. Recently, a new spline basis incorporating this zero integral property, called <math><mi>Z</mi> <mspace></mspace> <mi>B</mi></math> -splines, was developed. However, this basis does not possess the orthogonal property which is beneficial from computational and application point of view. As a result of this paper, we describe an efficient method for constructing an orthogonal <math><mi>Z</mi> <mspace></mspace> <mi>B</mi></math> -splines basis, called <math><mi>Z</mi> <mspace></mspace> <mi>B</mi></math> -splinets. The advantages of the <math><mi>Z</mi> <mspace></mspace> <mi>B</mi></math> -splinet approach are foremost a computational efficiency and locality of basis supports that is desirable for data interpretability, e.g. in the context of functional principal component analysis. The proposed approach is demonstrated on two empirical datasets.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 4","pages":"673-709"},"PeriodicalIF":1.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12985404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147468120","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

An improved LDA dimension reduction algorithm for multivariate time series classification. 多变量时间序列分类中一种改进的LDA降维算法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-07-10 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2530580

Hongyu Zhou, Yunling Kang, Guidong Liu, Guoqiao You

{"title":"An improved LDA dimension reduction algorithm for multivariate time series classification.","authors":"Hongyu Zhou, Yunling Kang, Guidong Liu, Guoqiao You","doi":"10.1080/02664763.2025.2530580","DOIUrl":"10.1080/02664763.2025.2530580","url":null,"abstract":"In recent years, multivariate time series (MTS) classification has gradually become a research hotspot. However, due to the high-dimensional nature of MTS, directly classifying them often leads to suboptimal results. As a result, existing methods typically apply dimension reduction to the MTS dataset before classification. But the traditional MTS dimension reduction methods often lead to significant information redundancy or loss when dealing with unequal-length MTS dataset. To minimize information loss, this paper proposes a novel extraction method that helps transform unequal-length MTS dataset into equal-length MTS dataset. Furthermore, since existing dimension reduction methods ignore the fact that different MTS may have the same feature points at different time moments, this paper proposes a supervised dimension reduction method based on Linear Discriminant Analysis (LDA). This method aims to find the projection plane at each time point that minimizes the within-class scatter and maximizes the between-class scatter, thereby improving the effectiveness of dimension reduction. Experiments were conducted on 16 publicly available datasets. The results show that the proposed method effectively enhances classification performance after dimension reduction, achieving good experimental results.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 4","pages":"659-672"},"PeriodicalIF":1.1,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12981257/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147468179","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

Forecasting hourly foodservice sales during geopolitical and economical disruption using zero-inflated mixed effects models. 使用零膨胀混合效应模型预测地缘政治和经济中断期间每小时餐饮服务销售。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-07-07 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2519136

Nathan A Judd, Kalliopi Mylona, Haiming Liu, Andy Hogg, Tim Butler

引用次数: 0

Discriminant analysis for a folded Watson distribution. 折叠沃森分布的判别分析。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-07-04 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2522769

Adelaide Figueiredo, Fernanda Figueiredo

引用次数: 0