The Annals of Applied Statistics最新文献_第2页

How are PreLaunch online movie reviews related to box office revenues? 预热在线电影评论与票房收入有何关系？

The Annals of Applied Statistics Pub Date : 2024-06-01 DOI: 10.1214/23-aoas1854

Tianyu Guan, Jason Ho, Robert Krider, Jiguo Cao, Andrew Fogg

引用次数: 0

A framework for analysing longitudinal data involving time-varying covariates 涉及时变协变量的纵向数据分析框架

The Annals of Applied Statistics Pub Date : 2024-06-01 DOI: 10.1214/23-aoas1851

Reza Drikvandi, G. Verbeke, G. Molenberghs

{"title":"A framework for analysing longitudinal data involving time-varying covariates","authors":"Reza Drikvandi, G. Verbeke, G. Molenberghs","doi":"10.1214/23-aoas1851","DOIUrl":"https://doi.org/10.1214/23-aoas1851","url":null,"abstract":"Standard models for longitudinal data ignore the stochastic nature of time-varying covariates and their stochastic evolution over time by treating them as fixed variables. There have been recent methods for modelling time-varying covariates, however those methods cannot be applied to analyse longitudinal data when the longitudinal response and the time-varying covariates for each subject are measured at different time points. Moreover, it is difficult to study the temporal effects of a time-varying covariate on the longitudinal response and the temporal correlation between them. Motivated by data from an AIDS cohort study conducted over 26 years at the University Hospitals Leuven in which the measurements on the CD4 cell count and viral load for patients are not taken at the same time point, we present a framework to address those challenges by using joint multivariate mixed models to jointly model time-varying covariates and a longitudinal response, instead of including time-varying covariates in the response model. This approach also has the advantage that one can study the association between the covariate at any time point and the response at any other time point, without having to explicitly model the conditional distribution of the response given the covariate. We use penalised spline functions of time to capture the evolutions of both the response and time-varying covariates over time.","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"7 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141229376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Penalized joint models of high-dimensional longitudinal biomarkers and a survival outcome 高维纵向生物标记物和生存结果的惩罚性联合模型

The Annals of Applied Statistics Pub Date : 2024-06-01 DOI: 10.1214/23-aoas1844

Jiehuan Sun, Sanjib Basu

引用次数: 0

Assessing screening efficacy in the presence of cancer overdiagnosis 在癌症过度诊断的情况下评估筛查效果

The Annals of Applied Statistics Pub Date : 2024-06-01 DOI: 10.1214/23-aoas1848

Ying Huang, Ziding Feng

引用次数: 0

Risk-aware restricted outcome learning for individualized treatment regimes of schizophrenia 针对精神分裂症个体化治疗方案的风险意识限制性结果学习

The Annals of Applied Statistics Pub Date : 2024-06-01 DOI: 10.1214/23-aoas1836

Shuying Zhu, Weining Shen, Haoda Fu, Annie Qu

引用次数: 0

Semiparametric bivariate hierarchical state space model with application to hormone circadian relationship 应用于激素昼夜节律关系的半参数双变量分层状态空间模型

The Annals of Applied Statistics Pub Date : 2024-06-01 DOI: 10.1214/23-aoas1834

Mengying You, Wensheng Guo

引用次数: 0

Filtrated common functional principal component analysis of multigroup functional data 对多组函数数据进行过滤式通用函数主成分分析

The Annals of Applied Statistics Pub Date : 2024-06-01 DOI: 10.1214/23-aoas1827

Shuhao Jiao, Ron Frostig, H. Ombao

{"title":"Filtrated common functional principal component analysis of multigroup functional data","authors":"Shuhao Jiao, Ron Frostig, H. Ombao","doi":"10.1214/23-aoas1827","DOIUrl":"https://doi.org/10.1214/23-aoas1827","url":null,"abstract":"Local ﬁeld potentials (LFPs) are signals that measure electrical activities in localized cortical regions and are collected from multiple tetrodes implanted across a patch on the surface of cortex. Hence, they can be treated as multigroup functional data, where the trajectories collected across temporal epochs from one tetrode are viewed as a group of functions. In many cases multitetrode LFP trajectories contain both global variation patterns (which are shared by most groups, due to signal synchrony) and idiosyncratic variation patterns (common only to a small subset of groups), and such structure is very informative to the data mechanism. Therefore, one goal in this paper is to develop an efﬁcient algorithm that is able to capture and quantify both global and idiosyncratic features. We develop the novel ﬁltrated common functional principal components (ﬁlt-fPCA) method, which is a novel forest-structured fPCA for multigroup functional data. A major advantage of the proposed ﬁlt-fPCA method is its ability to extract the common components in a ﬂexible “multiresolution” manner. The proposed approach is highly data-driven, and no prior knowledge of “ground-truth” data structure is needed, making it suitable for analyzing complex multigroup functional data. In addition, the ﬁlt-fPCA method is able to produce parsimonious, interpretable, and efﬁcient functional reconstruction (low reconstruction error) for multigroup functional data with orthonormal basis functions. Here the proposed ﬁlt-fPCA method is employed to study the impact of a shock (induced stroke) on the synchrony structure of rat brain. The proposed ﬁlt-fPCA is general and inclusive that can be readily applied to analyze any multigroup functional data, such as multivariate functional data, spatial-temporal data, and longitudinal functional data.","PeriodicalId":188068,"journal":{"name":"The Annals of Applied Statistics","volume":"80 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141412015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Density-based matching rule: Optimality, estimation, and application in forensic problems 基于密度的匹配规则：最优性、估计及在法证问题中的应用

The Annals of Applied Statistics Pub Date : 2024-03-01 DOI: 10.1214/23-aoas1812

Hana Lee, Yumou Qiu, Alicia Carriquiry, Danica M. Ommen

引用次数: 0

Network-level traffic flow prediction: Functional time series vs. functional neural network approach 网络级交通流量预测：功能时间序列与功能神经网络方法

The Annals of Applied Statistics Pub Date : 2024-03-01 DOI: 10.1214/23-aoas1795

Tao Ma, Fang Yao, Zhou Zhou

引用次数: 0

Learning healthcare delivery network with longitudinal electronic health records data 利用纵向电子健康记录数据学习医疗保健服务网络

The Annals of Applied Statistics Pub Date : 2024-03-01 DOI: 10.1214/23-aoas1818

Jiehuan Sun, K. Liao, Tianxi Cai

引用次数: 0