Journal of Applied Statistics最新文献_第5页

New alpha power unit distribution: properties and application. 新型阿尔法动力单元分布：特性与应用。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-10-29 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2578657

Abdus Saboor, Farrukh Jamal, Arshid Khan, Indranil Ghosh

{"title":"New alpha power unit distribution: properties and application.","authors":"Abdus Saboor, Farrukh Jamal, Arshid Khan, Indranil Ghosh","doi":"10.1080/02664763.2025.2578657","DOIUrl":"10.1080/02664763.2025.2578657","url":null,"abstract":"In this article, an absolutely continuous two parameter alpha power unit (APU, in short) probability model is introduced using the method of adding an extra parameter developed originally by Mahdavi and Kundu [A new method for generating distributions with an application to exponential distribution, Comm. Stat. Theory Methods 46 (2017), pp. 6543-6557]. The derived probability model appears to be more flexible than some of the existing well-known univariate distributions defined on the unit interval due to its variety of shapes of the hazard function. Several useful structural properties of the APU distribution are discussed, including quantile function, residual life function, moments, stochastic ordering and order statistics. The maximum likelihood estimators of unknown parameters cannot be obtained in explicit forms, and they have to be obtained by solving non-linear equations only. A simulation study has been conducted to check the consistency of the adopted MLE procedure. Two real-life data sets have been re-analyzed to show how the proposed probability model works in practice.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 10","pages":"1962-1984"},"PeriodicalIF":1.1,"publicationDate":"2025-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13421120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148630559","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

DeepVARwT: deep learning for a VAR model with trend. DeepVARwT：对带有趋势的VAR模型进行深度学习。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-10-29 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2577657

Xixi Li, Jingsong Yuan

引用次数: 0

Bayesian hierarchical time-varying distributed lag nonlinear model: applications in the short-term association between ambient temperature and daily confirmed cases of COVID-19. 贝叶斯分层时变分布滞后非线性模型在环境温度与新冠肺炎日确诊病例短期关联中的应用

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-10-24 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2577659

Dongu Han, Kiljae Lee, Yeonseung Chung, Genya Kobayashi, Taeryon Choi

{"title":"Bayesian hierarchical time-varying distributed lag nonlinear model: applications in the short-term association between ambient temperature and daily confirmed cases of COVID-19.","authors":"Dongu Han, Kiljae Lee, Yeonseung Chung, Genya Kobayashi, Taeryon Choi","doi":"10.1080/02664763.2025.2577659","DOIUrl":"10.1080/02664763.2025.2577659","url":null,"abstract":"Environmental epidemiological studies are often interested in the short-term association between environmental exposure and health responses through daily time-series data analysis across multiple locations. The distributed lag nonlinear model is one of the most frequently adopted approaches to estimate the association through the exposure-response function. Our study proposes a novel Bayesian methodology for estimating the exposure-response functions for multiple subjects through a hierarchical approach while incorporating decaying lag effects and considering intertwined nonadditive interactions in the exposure, lag, and time dimensions through a negative binomial distribution. We develop Markov Chain Monte Carlo and variational Bayes algorithms for estimation. To validate the methodology, we provide some empirical results using simulated datasets and a real dataset to investigate the short-term relationship between ambient temperature and COVID-19 incidence in the United States.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 10","pages":"1916-1942"},"PeriodicalIF":1.1,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13421134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148630584","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

Bias reduction methods in the hierarchical likelihood approach for shared frailty model of clustered failure time data. 聚类故障时间数据共享脆弱性模型的层次似然方法中的偏置减少方法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-10-24 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2575033

Adams Kusi Appiah, Hongying Dai, Gleb Haynatzki

{"title":"Bias reduction methods in the hierarchical likelihood approach for shared frailty model of clustered failure time data.","authors":"Adams Kusi Appiah, Hongying Dai, Gleb Haynatzki","doi":"10.1080/02664763.2025.2575033","DOIUrl":"10.1080/02664763.2025.2575033","url":null,"abstract":"Shared frailty models are widely used for the analyses of multivariate survival data. A hierarchical likelihood (h-likelihood) approach has been developed for estimating the regression parameters and frailty variates, in which the latent frailties are treated as 'parameters' and estimated jointly with other parameters of interest. The h-likelihood estimators generally perform well in various frailty models. However, they are known to be biased for non-normal random effects. Existing modifications to the h-likelihood employ the total derivative and second-order Laplace approximation, which is computationally intensive with complicated mathematical derivations. In this work, we propose two effective bias correction methods for the h-likelihood estimators under the shared gamma frailty models. The first method modifies the adjusted profile likelihood by adding a logarithmic transformation of the variance of the frailty parameter to avoid zero estimates in the frailty parameter. The second approach modifies the score function of the adjusted profile likelihood. Thus, in the two modifications, we avoid the use of the total derivative and second-order Laplace approximation. Simulation studies show that the proposed approaches reduce the bias in the h-likelihood estimators, especially for the estimate of the frailty parameter. Applications of both methods are illustrated using recurrent kidney infection data.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 10","pages":"1803-1831"},"PeriodicalIF":1.1,"publicationDate":"2025-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13421123/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148630556","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 review and comparison of methods of testing for heteroskedasticity in the linear regression model. 线性回归模型中异方差检验方法的综述与比较。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-10-24 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2025.2575038

Thomas Farrar, Renette Blignaut, Retha Luus, Sarel Steel

引用次数: 0

Modeling multivariate ordinal time series. 多元有序时间序列建模。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-10-18 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2575034

Malte Jahn, Christian H Weiß

引用次数: 0

Homogeneity of multinomial populations when data are classified into a large number of groups. 当数据被划分为大量组时，多项总体的同质性。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-10-07 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2565604

M V Alba-Fernández, M D Jiménez-Gamero, F J Ariza-López

{"title":"Homogeneity of multinomial populations when data are classified into a large number of groups.","authors":"M V Alba-Fernández, M D Jiménez-Gamero, F J Ariza-López","doi":"10.1080/02664763.2025.2565604","DOIUrl":"10.1080/02664763.2025.2565604","url":null,"abstract":"Suppose that we are interested in the comparison of two independent categorical variables. Suppose also that the population is divided into subpopulations or groups. Notice that the distribution of the target variable may vary across subpopulations, moreover, it might happen that the two independent variables have the same distribution in the whole population, but their distributions could differ in some groups. So, instead of testing the homogeneity of the two categorical variables, one may be interested in simultaneously testing the homogeneity in all groups. A novel procedure is proposed for carrying out such a testing problem. The test statistic is shown to be asymptotically normal, avoiding the use of complicated resampling methods to get p-values. Here by asymptotic we mean when the number of groups increases; the sample sizes of the data from each group can either stay bounded or grow with the number of groups. The finite sample performance of the proposal is empirically evaluated through an extensive simulation study. The usefulness of the proposal is illustrated by three data sets coming from diverse experimental fields such as education, the COVID-19 pandemic and digital elevation models.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 8","pages":"1442-1462"},"PeriodicalIF":1.1,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13248512/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148217880","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 partially-protected regularization as a model selection tool. 贝叶斯部分保护正则化作为模型选择工具。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-10-07 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2559025

Yasir Atalan, Selim Yaman, Jeff Gill

引用次数: 0

An improved two-stage binary relevance method for multilabel classification. 一种改进的两阶段二值相关多标签分类方法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-10-04 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2567977

Ziyue Chen, Qing Wang

{"title":"An improved two-stage binary relevance method for multilabel classification.","authors":"Ziyue Chen, Qing Wang","doi":"10.1080/02664763.2025.2567977","DOIUrl":"10.1080/02664763.2025.2567977","url":null,"abstract":"Multilabel classification concerns a family of unconventional classification problems, where each instance may be associated with multiple labels simultaneously. One of the traditional methods for multilabel classification is the binary relevance algorithm, in which one first converts the multilabel dataset into multiple binary-outcome datasets, one for each label, then trains a binary classifier on every transformed dataset and predicts the occurrence of the given label, and in the end aggregates all predicted labels together to compose a multilabel outcome. Two significant drawbacks of this method include its failure to account for label correlation and its poor predictive performance when the labels are sparse. To address these pitfalls, we developed an improved two-stage binary relevance method that utilizes cluster analysis to unveil underlying label structures and subsequently realizes label subset and individual label predictions in a sequential manner. In an effort to alleviate the challenge resulted from label subset imbalance, we consider several refined realizations of the proposed methodology. We showcase the proposal on ten real multilabel datasets and compare it to the benchmark binary relevance method as well as three other competing methods using five different performance metrics. The numerical studies reveal that the proposal yields significantly better or comparable results to its counterparts in almost all cases.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 8","pages":"1493-1514"},"PeriodicalIF":1.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13248491/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148217737","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

Inference for dependent competing risks model under m-cycle minimum ranked set sampling. m周期最小排序集抽样下依赖竞争风险模型的推理。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-10-04 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2567976

Jiaxin Zhang, Wenhao Gui

{"title":"Inference for dependent competing risks model under m-cycle minimum ranked set sampling.","authors":"Jiaxin Zhang, Wenhao Gui","doi":"10.1080/02664763.2025.2567976","DOIUrl":"10.1080/02664763.2025.2567976","url":null,"abstract":"Minimum ranked set sampling offers an effective approach for collecting failure time data while optimizing testing resources. This paper examines dependent competing risks model within the context of m-cycle minimum ranked set sampling data. Assuming that component lifetimes adhere to a two-parameter generalized inverted exponential distribution, we develop dependence structures utilizing the Marshall-Olkin distribution framework. The study establishes maximum likelihood estimation and Bayesian inference procedures under both unrestricted parameters and ordered restrictions. The theoretical analysis confirms the existence and uniqueness conditions for maximum likelihood estimators, with corresponding interval estimators being subsequently derived. For Bayesian inference, we derive posterior estimates under flexible prior specifications, employing Metropolis-Hastings and importance sampling algorithms to address complex posterior calculations. Through comprehensive numerical simulations and real-world case analysis, this study systematically evaluates the comparative performance of different estimation approaches while examining how cyclic sampling strategies influence estimation precision. Finally, implementation guidelines and production-oriented conclusions are provided based on the study results.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 8","pages":"1463-1492"},"PeriodicalIF":1.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13248504/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148218022","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