Journal of Applied Statistics最新文献_第6页

Semiparametric regression analysis of panel binary data with a dependent failure time. 具有相关失效时间的面板二值数据的半参数回归分析。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-19 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2428266

Lei Ge, Yang Li, Jianguo Sun

{"title":"Semiparametric regression analysis of panel binary data with a dependent failure time.","authors":"Lei Ge, Yang Li, Jianguo Sun","doi":"10.1080/02664763.2024.2428266","DOIUrl":"10.1080/02664763.2024.2428266","url":null,"abstract":"In health and clinical research, panel binary data from recurrent events arise when subjects are surveyed to report occurrence statuses of recurrent events over fixed observation windows. In practice, such data can be cut short by a dependent failure event such as death. For the analysis of panel binary data, tools from generalized linear models overlook the recurrence nature of panel binary data, and other relevant literature does not accommodate the failure time. Motivated by the hospitalization data surveyed from the Health and Retirement Study, we propose a semiparametric joint-modeling-based procedure for analyzing panel binary data with a dependent failure time. For model fitting, we develop a computationally efficient EM algorithm and show the resulting estimates are consistent and asymptotically normal. Theoretical results are provided to enable valid inferences. Simulation studies have confirmed the performance of the proposed method in practical settings. The method is applied to assess important risk factors associated with incidences of hospitalization among the working elderly.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 7","pages":"1423-1445"},"PeriodicalIF":1.2,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12117875/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144179896","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 bootstrap procedure to estimate the causal effect of a public policy, considering overlap and imperfect compliance. 一种评估公共政策因果效应的自举程序，考虑到重叠和不完全遵从。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-17 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2428994

Stefano Cabras

引用次数: 0

A defective cure rate quantile regression model for male breast cancer data. 男性乳腺癌数据的缺陷治愈率分位数回归模型。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-14 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2428272

Agatha Rodrigues, Patrick Borges, Bruno Santos

引用次数: 0

Efficient non-parametric estimation of variable productivity Hawkes processes. 可变生产率Hawkes过程的有效非参数估计。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-12 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2426019

Sophie Phillips, Frederic Schoenberg

引用次数: 0

Forecasting economic growth by combining local linear and standard approaches. 结合本地线性和标准方法预测经济增长。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-08 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2424920

Marlon Fritz, Sarah Forstinger, Yuanhua Feng, Thomas Gries

{"title":"Forecasting economic growth by combining local linear and standard approaches.","authors":"Marlon Fritz, Sarah Forstinger, Yuanhua Feng, Thomas Gries","doi":"10.1080/02664763.2024.2424920","DOIUrl":"https://doi.org/10.1080/02664763.2024.2424920","url":null,"abstract":"Today, developing economies are of major importance for global macroeconomic development. However, the empirical analysis and especially the forecasting of macroeconomic time series remain difficult due to a lack of sufficient data, data frequency, high volatility, and non-linear developments. These difficulties require more sophisticated approaches to obtain reliable forecasts. Therefore, we propose an improved forecasting method especially for growth data based on a data-driven local linear trend estimation with an extended iterative plug-in algorithm for determining the bandwidth endogenously. This approach allows a smooth trend estimation that takes care of temporary changes in trend processes. Further, the naïve random walk model is extended for forecasting by including a local linear, time-varying drift. We apply this method to GDP development for six developing and two advanced economies and compare different forecast combinations. The combinations that include the local linear approach and the random walk with a local linear trend improve forecasting accuracy and reduce variance.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 7","pages":"1342-1360"},"PeriodicalIF":1.2,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12117868/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144182001","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 robust distance-based approach for detecting multidimensional outliers. 一种基于距离的检测多维异常值的鲁棒方法。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-07 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2422403

R Lakshmi, T A Sajesh

{"title":"A robust distance-based approach for detecting multidimensional outliers.","authors":"R Lakshmi, T A Sajesh","doi":"10.1080/02664763.2024.2422403","DOIUrl":"10.1080/02664763.2024.2422403","url":null,"abstract":"Identifying outliers in data analysis is a critical task, as outliers can significantly influence the results and conclusions drawn from a dataset. This study explores the use of the Mahalanobis distance metric for detecting outliers in multivariate data, focusing on a novel approach inspired by the work of M. Falk, [On mad and comedians, Ann. Inst. Stat. Math. 49 (1997), pp. 615-644]. The proposed method is rigorously tested through extensive simulation analysis, where it demonstrates high True Positive Rates (TPR) and low False Positive Rates (FPR) when compared to other existing outlier detection techniques. Through extensive simulation analysis, we empirically evaluate the affine equivariance and breakdown properties of our proposed distance measure and it is evident from the outputs that our robust distance measure demonstrates effective results with respect to the measures FPR and TPR. The proposed method was applied to seven different datasets, showing promising true positive rates (TPR) and false positive rates (FPR), and it outperformed several well-known outlier identification approaches. We can effectively use our proposed distance measure in fields demanding outlier detection.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 6","pages":"1278-1298"},"PeriodicalIF":1.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144016593","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

Clustering in point processes on linear networks using nearest neighbour volumes. 基于最近邻体积的线性网络点过程聚类。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-07 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2411214

Juan F Díaz-Sepúlveda, Nicoletta D'Angelo, Giada Adelfio, Jonatan A González, Francisco J Rodríguez-Cortés

{"title":"Clustering in point processes on linear networks using nearest neighbour volumes.","authors":"Juan F Díaz-Sepúlveda, Nicoletta D'Angelo, Giada Adelfio, Jonatan A González, Francisco J Rodríguez-Cortés","doi":"10.1080/02664763.2024.2411214","DOIUrl":"10.1080/02664763.2024.2411214","url":null,"abstract":"This study introduces a novel method specifically designed to detect clusters of points within linear networks. This method extends a classification approach used for point processes in spatial contexts. Unlike traditional methods that operate on planar spaces, our approach adapts to the unique geometric challenges of linear networks, where classical properties of point processes are altered, and intuitive data visualisation becomes more complex. Our method utilises the distribution of the Kth nearest neighbour volumes, extending planar-based clustering techniques to identify regions of increased point density within a network. This approach is particularly effective for distinguishing overlapping Poisson processes within the same linear network. We demonstrate the practical utility of our method through applications to road traffic accident data from two Colombian cities, Bogota and Medellin. Our results reveal distinct clusters of high-density points in road segments where severe traffic accidents (resulting in injuries or fatalities) are most likely to occur, highlighting areas of increased risk. These clusters were primarily located on major arterial roads with high traffic volumes. In contrast, low-density points corresponded to areas with fewer accidents, likely due to lower traffic flow or other mitigating factors. Our findings provide valuable insights for urban planning and road safety management.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 5","pages":"993-1016"},"PeriodicalIF":1.2,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951330/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752900","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

Robust estimation of the incubation period and the time of exposure using γ-divergence. 利用γ-散度稳健估计潜伏期和暴露时间。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-06 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2420221

Daisuke Yoneoka, Takayuki Kawashima, Yuta Tanoue, Shuhei Nomura, Akifumi Eguchi

{"title":"Robust estimation of the incubation period and the time of exposure using γ-divergence.","authors":"Daisuke Yoneoka, Takayuki Kawashima, Yuta Tanoue, Shuhei Nomura, Akifumi Eguchi","doi":"10.1080/02664763.2024.2420221","DOIUrl":"https://doi.org/10.1080/02664763.2024.2420221","url":null,"abstract":"Estimating the exposure time to single infectious pathogens and the associated incubation period, based on symptom onset data, is crucial for identifying infection sources and implementing public health interventions. However, data from rapid surveillance systems designed for early outbreak warning often come with outliers originated from individuals who were not directly exposed to the initial source of infection (i.e. tertiary and subsequent infection cases), making the estimation of exposure time challenging. To address this issue, this study uses a three-parameter lognormal distribution and proposes a new γ-divergence-based robust approach for estimating the parameter corresponding to exposure time with a tailored optimization procedure using the majorization-minimization algorithm, which ensures the monotonic decreasing property of the objective function. Comprehensive numerical experiments and real data analyses suggest that our method is superior to conventional methods in terms of bias, mean squared error, and coverage probability of 95% confidence intervals.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 6","pages":"1239-1257"},"PeriodicalIF":1.2,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12035932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143992898","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 optimal subsampling design for large-scale Cox model with censored data. 带截尾数据的大尺度Cox模型的最优次抽样设计。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-04 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2423234

Shiqi Liu, Zilong Xie, Ming Zheng, Wen Yu

{"title":"An optimal subsampling design for large-scale Cox model with censored data.","authors":"Shiqi Liu, Zilong Xie, Ming Zheng, Wen Yu","doi":"10.1080/02664763.2024.2423234","DOIUrl":"10.1080/02664763.2024.2423234","url":null,"abstract":"Subsampling designs are useful for reducing computational load and storage cost for large-scale data analysis. For massive survival data with right censoring, we propose a class of optimal subsampling designs under the widely-used Cox model. The proposed designs utilize information from both the outcome and the covariates. Different forms of the design can be derived adaptively to meet various targets, such as optimizing the overall estimation accuracy or minimizing the variation of specific linear combination of the estimators. Given the subsampled data, the inverse probability weighting approach is employed to estimate the model parameters. The resultant estimators are shown to be consistent and asymptotically normally distributed. Simulation results indicate that the proposed subsampling design yields more efficient estimators than the uniform subsampling by using subsampled data of comparable sample sizes. Additionally, the subsampling estimation significantly reduces the computational load and storage cost relative to the full data estimation. An analysis of a real data example is provided for illustration.","PeriodicalId":15239,"journal":{"name":"Journal of Applied Statistics","volume":"52 7","pages":"1315-1341"},"PeriodicalIF":1.2,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12123965/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199240","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

Efficient fully Bayesian approach to brain activity mapping with complex-valued fMRI data. 利用复杂值fMRI数据进行脑活动映射的高效全贝叶斯方法。

IF 1.2 4区数学

Journal of Applied Statistics Pub Date : 2024-11-04 eCollection Date: 2025-01-01 DOI: 10.1080/02664763.2024.2422392

Zhengxin Wang, Daniel B Rowe, Xinyi Li, D Andrew Brown

引用次数: 0