Journal of the Royal Statistical Society Series C-Applied Statistics最新文献

Inverse set estimation and inversion of simultaneous confidence intervals. 反集估计和同时置信区间反演。

IF 1 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-05-31 eCollection Date: 2024-08-01 DOI: 10.1093/jrsssc/qlae027

Junting Ren, Fabian J E Telschow, Armin Schwartzman

引用次数: 0

Population-level task-evoked functional connectivity via Fourier analysis. 通过傅立叶分析实现群体级任务诱发功能连接。

IF 1 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-03-14 eCollection Date: 2024-08-01 DOI: 10.1093/jrsssc/qlae015

Kun Meng, Ani Eloyan

引用次数: 0

Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies. 测试移动健康研究单变量时间序列中存在缺失数据时的单位根非平稳性。

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-02-29 eCollection Date: 2024-06-01 DOI: 10.1093/jrsssc/qlae010

Charlotte Fowler, Xiaoxuan Cai, Justin T Baker, Jukka-Pekka Onnela, Linda Valeri

{"title":"Testing unit root non-stationarity in the presence of missing data in univariate time series of mobile health studies.","authors":"Charlotte Fowler, Xiaoxuan Cai, Justin T Baker, Jukka-Pekka Onnela, Linda Valeri","doi":"10.1093/jrsssc/qlae010","DOIUrl":"10.1093/jrsssc/qlae010","url":null,"abstract":"The use of digital devices to collect data in mobile health studies introduces a novel application of time series methods, with the constraint of potential data missing at random or missing not at random (MNAR). In time-series analysis, testing for stationarity is an important preliminary step to inform appropriate subsequent analyses. The Dickey-Fuller test evaluates the null hypothesis of unit root non-stationarity, under no missing data. Beyond recommendations under data missing completely at random for complete case analysis or last observation carry forward imputation, researchers have not extended unit root non-stationarity testing to more complex missing data mechanisms. Multiple imputation with chained equations, Kalman smoothing imputation, and linear interpolation have also been used for time-series data, however such methods impose constraints on the autocorrelation structure and impact unit root testing. We propose maximum likelihood estimation and multiple imputation using state space model approaches to adapt the augmented Dickey-Fuller test to a context with missing data. We further develop sensitivity analyses to examine the impact of MNAR data. We evaluate the performance of existing and proposed methods across missing mechanisms in extensive simulations and in their application to a multi-year smartphone study of bipolar patients.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11175825/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332377","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

Revisiting the effects of maternal education on adolescents' academic performance: Doubly robust estimation in a network-based observational study. 重新审视母亲教育对青少年学习成绩的影响：基于网络的观察研究中的双稳健估计。

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-02-13 eCollection Date: 2024-06-01 DOI: 10.1093/jrsssc/qlae008

Vanessa McNealis, Erica E M Moodie, Nema Dean

{"title":"Revisiting the effects of maternal education on adolescents' academic performance: Doubly robust estimation in a network-based observational study.","authors":"Vanessa McNealis, Erica E M Moodie, Nema Dean","doi":"10.1093/jrsssc/qlae008","DOIUrl":"10.1093/jrsssc/qlae008","url":null,"abstract":"In many contexts, particularly when study subjects are adolescents, peer effects can invalidate typical statistical requirements in the data. For instance, it is plausible that a student's academic performance is influenced both by their own mother's educational level as well as that of their peers. Since the underlying social network is measured, the Add Health study provides a unique opportunity to examine the impact of maternal college education on adolescent school performance, both direct and indirect. However, causal inference on populations embedded in social networks poses technical challenges, since the typical no interference assumption no longer holds. While inverse probability-of-treatment weighted (IPW) estimators have been developed for this setting, they are often highly unstable. Motivated by the question of maternal education, we propose doubly robust (DR) estimators combining models for treatment and outcome that are consistent and asymptotically normal if either model is correctly specified. We present empirical results that illustrate the DR property and the efficiency gain of DR over IPW estimators even when the treatment model is misspecified. Contrary to previous studies, our robust analysis does not provide evidence of an indirect effect of maternal education on academic performance within adolescents' social circles in Add Health.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11175826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332376","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

Unsupervised Bayesian classification for models with scalar and functional covariates. 针对标量和功能协变量模型的无监督贝叶斯分类。

IF 1 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-02-07 eCollection Date: 2024-06-01 DOI: 10.1093/jrsssc/qlae006

Nancy L Garcia, Mariana Rodrigues-Motta, Helio S Migon, Eva Petkova, Thaddeus Tarpey, R Todd Ogden, Julio O Giordano, Martin M Perez

{"title":"Unsupervised Bayesian classification for models with scalar and functional covariates.","authors":"Nancy L Garcia, Mariana Rodrigues-Motta, Helio S Migon, Eva Petkova, Thaddeus Tarpey, R Todd Ogden, Julio O Giordano, Martin M Perez","doi":"10.1093/jrsssc/qlae006","DOIUrl":"10.1093/jrsssc/qlae006","url":null,"abstract":"We consider unsupervised classification by means of a latent multinomial variable which categorizes a scalar response into one of the L components of a mixture model which incorporates scalar and functional covariates. This process can be thought as a hierarchical model with the first level modelling a scalar response according to a mixture of parametric distributions and the second level modelling the mixture probabilities by means of a generalized linear model with functional and scalar covariates. The traditional approach of treating functional covariates as vectors not only suffers from the curse of dimensionality, since functional covariates can be measured at very small intervals leading to a highly parametrized model, but also does not take into account the nature of the data. We use basis expansions to reduce the dimensionality and a Bayesian approach for estimating the parameters while providing predictions of the latent classification vector. The method is motivated by two data examples that are not easily handled by existing methods. The first example concerns identifying placebo responders on a clinical trial (normal mixture model) and the other predicting illness for milking cows (zero-inflated mixture of the Poisson model).","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11271982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789691","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 inference for clustered recurrent events with zero inflation and a terminal event. 针对零膨胀和终结事件的群集重复事件的贝叶斯半参数推断。

IF 1 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-02-01 eCollection Date: 2024-06-01 DOI: 10.1093/jrsssc/qlae003

Xinyuan Tian, Maria Ciarleglio, Jiachen Cai, Erich J Greene, Denise Esserman, Fan Li, Yize Zhao

引用次数: 0

A Bayesian latent class model for integrating multi-source longitudinal data: application to the CHILD cohort study 整合多源纵向数据的贝叶斯潜类模型:在儿童队列研究中的应用

4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-11-13 DOI: 10.1093/jrsssc/qlad100

Zihang Lu, Padmaja Subbarao, Wendy Lou

引用次数: 1

CRP-Tree: a phylogenetic association test for binary traits CRP-Tree:一种二元性状的系统发育关联试验

4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-11-13 DOI: 10.1093/jrsssc/qlad098

Julie Zhang, Gabriel A Preising, Molly Schumer, Julia A Palacios

{"title":"CRP-Tree: a phylogenetic association test for binary traits","authors":"Julie Zhang, Gabriel A Preising, Molly Schumer, Julia A Palacios","doi":"10.1093/jrsssc/qlad098","DOIUrl":"https://doi.org/10.1093/jrsssc/qlad098","url":null,"abstract":"Abstract An important problem in evolutionary genomics is to investigate whether a certain trait measured on each sample is associated with the sample phylogenetic tree. The phylogenetic tree represents the shared evolutionary history of the samples and it is usually estimated from molecular sequence data at a locus or from other type of genetic data. We propose a model for trait evolution inspired by the Chinese Restaurant Process that includes a parameter that controls the degree of preferential attachment, that is, the tendency of nodes in the tree to subtend from nodes of the same type. This model with no preferential attachment is equivalent to a structured coalescent model with simultaneous migration and coalescence events and serves as a null model. We derive a test for phylogenetic binary trait association with linear computational complexity and empirically demonstrate that it is more powerful than some other methods. We apply our test to study the phylogenetic association of some traits in swordtail fish, breast cancer, yellow fever virus, and influenza A H1N1 virus. R-package implementation of our methods is available at https://github.com/jyzhang27/CRPTree.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Bayesian kernel machine regression for count data: modelling the association between social vulnerability and COVID-19 deaths in South Carolina 计数数据的贝叶斯核机回归:模拟南卡罗来纳州社会脆弱性与COVID-19死亡之间的关系

4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-11-03 DOI: 10.1093/jrsssc/qlad094

Fedelis Mutiso, Hong Li, John L Pearce, Sara E Benjamin-Neelon, Noel T Mueller, Brian Neelon

{"title":"Bayesian kernel machine regression for count data: modelling the association between social vulnerability and COVID-19 deaths in South Carolina","authors":"Fedelis Mutiso, Hong Li, John L Pearce, Sara E Benjamin-Neelon, Noel T Mueller, Brian Neelon","doi":"10.1093/jrsssc/qlad094","DOIUrl":"https://doi.org/10.1093/jrsssc/qlad094","url":null,"abstract":"Abstract The COVID-19 pandemic created an unprecedented global health crisis. Recent studies suggest that socially vulnerable communities were disproportionately impacted, although findings are mixed. To quantify social vulnerability in the US, many studies rely on the Social Vulnerability Index (SVI), a county-level measure comprising 15 census variables. Typically, the SVI is modelled in an additive manner, which may obscure non-linear or interactive associations, further contributing to inconsistent findings. As a more robust alternative, we propose a negative binomial Bayesian kernel machine regression (BKMR) model to investigate dynamic associations between social vulnerability and COVID-19 death rates, thus extending BKMR to the count data setting. The model produces a ‘vulnerability effect’ that quantifies the impact of vulnerability on COVID-19 death rates in each county. The method can also identify the relative importance of various SVI variables and make future predictions as county vulnerability profiles evolve. To capture spatio-temporal heterogeneity, the model incorporates spatial effects, county-level covariates, and smooth temporal functions. For Bayesian computation, we propose a tractable data-augmented Gibbs sampler. We conduct a simulation study to highlight the approach and apply the method to a study of COVID-19 deaths in the US state of South Carolina during the 2021 calendar year.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135874680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel agreement statistic using data on uncertainty in ratings. 使用评分不确定性数据的新型一致性统计。

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-11-01 Epub Date: 2023-07-15 DOI: 10.1093/jrsssc/qlad063

Jarcy Zee, Laura Mariani, Laura Barisoni, Parag Mahajan, Brenda Gillespie

引用次数: 0