Journal of Applied Statistics最新文献_第6页

Distribution-valued data graphical model estimation based on M-LDQ feature embedding. 基于M-LDQ特征嵌入的分布值数据图形模型估计。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-10-04 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2567980

Qiying Wu, Huiwen Wang, Shan Lu

{"title":"Distribution-valued data graphical model estimation based on M-LDQ feature embedding.","authors":"Qiying Wu, Huiwen Wang, Shan Lu","doi":"10.1080/02664763.2025.2567980","DOIUrl":"10.1080/02664763.2025.2567980","url":null,"abstract":"Understanding and modeling distribution-valued data, an important form of symbolic data, has garnered significant attention in statistics because of its effectiveness in handling large datasets. Conventional statistical inference methods are not directly applicable to distribution-valued data, which has prompted extensive research efforts aimed at addressing this challenge. However, graphical models, which are powerful tools in applied statistics, have not yet been fully developed for distribution-valued data. To fill this gap, this study proposes a novel nonparametric graphical model estimation method for distribution-valued data. The proposed method first removes the inherent constraints of distributions, effectively capturing both position information (as a scalar) and shape information (as a function). We subsequently propose an aggregation method, which is based on the conditional independence test, to integrate the position information and shape information for graphical model estimation. Several numerical simulations have validated that our method outperforms other potential competing methods. Furthermore, we apply our method to construct the network of stocks that constitute the SSE 50 Index using daily distribution-valued data of five-minute returns. The empirical results reveal sector-specific relationships as well as cross-sector influences, highlighting the evolving interconnections between stocks from different sectors over time.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 8","pages":"1538-1561"},"PeriodicalIF":1.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13248500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148217912","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 integer-valued spatial autoregressive model with application to COVID-19 counts. 应用于COVID-19计数的整数值空间自回归模型。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-09-30 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2565593

Kai Yang, Mingming Jia, Xiaogang Dong

引用次数: 0

Sparse long-only Markowitz portfolio optimization. 稀疏只做多的Markowitz投资组合优化。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-09-29 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2565597

Tianci Qian

引用次数: 0

Adjusted profile likelihood inference for the scale parameter of the Gumbel distribution. Gumbel分布尺度参数的调整剖面似然推断。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-09-29 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2565602

Ayman Baklizi

{"title":"Adjusted profile likelihood inference for the scale parameter of the Gumbel distribution.","authors":"Ayman Baklizi","doi":"10.1080/02664763.2025.2565602","DOIUrl":"10.1080/02664763.2025.2565602","url":null,"abstract":"We consider inference about the scale parameter of the Gumbel distribution. The maximum likelihood estimator of the scale parameter based on the profile likelihood is biased. This is because the profile likelihood function is not a genuine likelihood. To address this problem, adjustments of the profile likelihood function of the scale parameter were obtained with the purpose of obtaining improved estimators. The adjustments aim at approximating a conditional likelihood using the so called <math> <mrow><msup><mi>p</mi> <mo>∗</mo></msup> </mrow> </math> formula for the conditional distribution of the maximum likelihood estimator. The resulting point estimators are investigated and compared with the maximum likelihood estimator in terms of their biases and mean squared errors using simulation techniques. We derived confidence intervals based on the profile likelihood and its adjustments. The confidence intervals are investigated in terms of the attainment of lower, upper and total error rates in addition to their simulated expected lengths. Our investigations show that the adjustments improve the performance of point and interval estimators. The methods were applied to real data examples.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 8","pages":"1427-1441"},"PeriodicalIF":1.1,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13248489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148217744","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

Empirical determination of baseball eras: multivariate change point analysis in major league baseball. 棒球时代的实证决定：美国职棒大联盟的多元变化点分析。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-09-26 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2552723

Mena C R Whalen, Gregory J Matthews, Brian M Mills

{"title":"Empirical determination of baseball eras: multivariate change point analysis in major league baseball.","authors":"Mena C R Whalen, Gregory J Matthews, Brian M Mills","doi":"10.1080/02664763.2025.2552723","DOIUrl":"https://doi.org/10.1080/02664763.2025.2552723","url":null,"abstract":"We use multivariate change point analysis methods to identify not only mean shifts but also changes in variance across a wide array of statistical time series. Our primary objective is to empirically discern distinct eras in the evolution of baseball, shedding light on significant transformations in team performance and management strategies. We employ baseball statistics from the late 1800s to 2021, spanning over a century of the sport's history. Results confirm previous historical research, pinpointing well-known baseball eras, such as the Dead Ball Era, Integration Era, Steroid Era, and Post-Steroid Era. Moreover, the study investigates changes in team performance, effectively identifying periods of both dynasties and collapses within a team's history. The multivariate change point analysis proves to be a valuable tool for understanding the dynamics of baseball's evolution. The method offers a data-driven approach to unveil structural shifts in the sport's historical landscape, providing fresh insights into the impact of rule changes, player strategies, and external factors on baseball's evolution. This not only enhances our comprehension of baseball, showing more robust identification of eras than past univariate time series work, but also showcases the broader applicability of multivariate change point analysis in the domain of sports research and beyond.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 6","pages":"1158-1179"},"PeriodicalIF":1.1,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13134745/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147815553","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 survival modeling with mixtures of inverse Gaussian frailties. 混合高斯逆弱点的贝叶斯生存模型。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-09-26 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2560510

Gilbert Kiprotich, Shikhar Tyagi, Pedro L Ramos

引用次数: 0

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection. 评估纵向t淋巴细胞在合并或不合并结核感染的hiv感染儿童中作为免疫恢复的生物标志物的表现

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-09-24 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2562307

Musie Ghebremichael

{"title":"Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.","authors":"Musie Ghebremichael","doi":"10.1080/02664763.2025.2562307","DOIUrl":"10.1080/02664763.2025.2562307","url":null,"abstract":"In this paper, the receiver operating characteristic (ROC) curve was used to investigate the performance of longitudinal CD4+ T cell counts as a biomarker of disease recovery in HIV-TB co-infected children from sub-Saharan Africa. According to the standard of care for monitoring pediatric HIV infection, routine assessment of immunologic/virologic markers is required. However, due to the lack of well-equipped laboratories and trained personnel, monitoring these markers as regularly as recommended is not feasible in sub-Saharan Africa. Thus, in the absence of routine laboratory monitoring, methods enabling the prediction of patient outcomes from existing CD4+ T-cell data provide a cost-effective alternative to routine monitoring in the region. Simulation studies were carried out to evaluate the performance of the TB-specific ROC model. We found that post-treatment longitudinal CD4+ T cell count had a 79% (95% CI: 0.72-0.86) probability of correctly distinguishing a recovered from non-recovered TB patient compared to a probability of 70% (95% CI: 0.59-0.82) in TB negative patients. Our findings demonstrate that longitudinal CD4+ T-cell counts are reliable indicators of pediatric immune recovery and provide a cost-effective alternative for predicting patient outcomes in the absence of routine laboratory monitoring, particularly HIV RNA determination.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 8","pages":"1562-1577"},"PeriodicalIF":1.1,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13248490/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148217772","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 semi-parametric approaches to normal/independent and elliptical distributions. 正态/独立分布和椭圆分布的贝叶斯半参数方法。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-09-23 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2552728

M Remedios Sillero-Denamiel, J Miguel Marín, Pepa Ramírez-Cobo, Fabrizio Ruggeri, Michael P Wiper

引用次数: 0

A nonparametric clustering stopping rule based on spatial median. 基于空间中值的非参数聚类停止规则。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-09-20 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2557964

Hend Gabr, Brian H Willis, Mohammed Baragilly

{"title":"A nonparametric clustering stopping rule based on spatial median.","authors":"Hend Gabr, Brian H Willis, Mohammed Baragilly","doi":"10.1080/02664763.2025.2557964","DOIUrl":"10.1080/02664763.2025.2557964","url":null,"abstract":"In this work, we introduce a nonparametric clustering stopping rule algorithm based on the spatial median. Our proposed method aims to achieve a balance between the homogeneity within the clusters and the heterogeneity between clusters. The proposed algorithm maximises the ratio of the variation between clusters and the variation within clusters while adjusting for the number of clusters and number of observations. The proposed algorithm is robust against distributional assumptions and the presence of outliers. Simulations were used to validate the algorithm. We further evaluated the stability and the efficacy of the proposed algorithm using three real-world datasets. Moreover, we compared the performance of our model with 13 other traditional algorithms for determining the number of clusters. We found that the proposed algorithm outperformed 11 of the algorithms considered for comparison in terms of clustering number determination. The finding demonstrates that the proposed method provides a reliable alternative to determine the number of clusters for multivariate data. Highlights A nonparametric clustering stopping rule algorithm based on the spatial median.Considers balancing intra-cluster homogeneity and inter-cluster heterogeneity.Maximizes between-cluster to within-cluster variation.Accounts for the number of clusters and number of observations.Outperforms 11 out of 13 traditional algorithms in cluster analysis.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 7","pages":"1297-1315"},"PeriodicalIF":1.1,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13228190/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148156665","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 Bayesian spatially-clustered coefficient model with temporal structures for hepatitis A data in South Korea. 韩国甲型肝炎数据的时间结构贝叶斯空间聚类系数模型。

IF 1.1 4区数学

Journal of Applied Statistics Pub Date : 2025-09-17 eCollection Date: 2026-01-01 DOI: 10.1080/02664763.2025.2555593

Jaeseon Lee, Jungsoon Choi

{"title":"A Bayesian spatially-clustered coefficient model with temporal structures for hepatitis A data in South Korea.","authors":"Jaeseon Lee, Jungsoon Choi","doi":"10.1080/02664763.2025.2555593","DOIUrl":"10.1080/02664763.2025.2555593","url":null,"abstract":"Hepatitis A, a highly contagious and perilous viral liver infection, is globally widespread, with its data collected across spatial and temporal domains. Also, demographic and socioeconomic covariates, such as population density and per capita income, are gathered over space and time units. As a result, the association between infectious disease outcomes and risk factors may differ across space and time. Some sub-regions may have a heterogeneous association with others, while a homogeneous temporal structure may exist within certain sub-regions. Acknowledging the potential variability in these associations, this study focused on comprehending the spatio-temporal dynamics of hepatitis A through a statistical model. In this paper, we analyzed monthly hepatitis A infection counts in the Republic of Korea from January 2020 to December 2021 using a Bayesian spatio-temporal model. Specifically, we employed a Bayesian spatially-clustered coefficient model with temporal structures to estimate sub-regions with the temporally varying risk effects associated with hepatitis A. Our focus lies in utilizing the Bayesian spatio-temporal model to uncover insights into the spatio-temporally varying relationships between covariates and hepatitis A outcomes. Furthermore, we addressed the spatial confounding bias prevalent in common spatial models with space-time random components by incorporating two-stage framework within our analysis.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"53 7","pages":"1253-1273"},"PeriodicalIF":1.1,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13228189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148156576","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