Journal of the Royal Statistical Society Series C-Applied Statistics最新文献_第5页

Saralees Nadarajah’s contribution to the Discussion of ‘The First Discussion Meeting on Statistical aspects of climate change’ Saralees Nadarajah对“气候变化统计方面的第一次讨论会议”讨论的贡献

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-06-13 DOI: 10.1093/jrsssc/qlad053

S. Nadarajah

引用次数: 0

Association Plots: visualizing cluster-specific associations in high-dimensional correspondence analysis biplots 关联图:在高维对应分析双图中可视化特定集群的关联

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-06-08 DOI: 10.1093/jrsssc/qlad039

E. Gralinska, Martin Vingron

{"title":"Association Plots: visualizing cluster-specific associations in high-dimensional correspondence analysis biplots","authors":"E. Gralinska, Martin Vingron","doi":"10.1093/jrsssc/qlad039","DOIUrl":"https://doi.org/10.1093/jrsssc/qlad039","url":null,"abstract":"\u0000 In molecular biology, just as in many other fields of science, data often come in the form of matrices or contingency tables with many observations (rows) for a set of variables (columns). While projection methods like principal component analysis or correspondence analysis (CA) can be applied for obtaining an overview of such data, in cases where the matrix is very large the associated loss of information upon projection into two or three dimensions may be dramatic. However, when the set of variables can be grouped into clusters, this opens up a new angle on the data. We focus on the question of which observations are associated to a cluster and distinguish it from other clusters. CA employs a geometry geared towards answering this question. We exploit this feature in order to introduce Association Plots for visualizing cluster-specific observations in complex data. Regardless of the data matrix dimensionality Association Plots are two-dimensional and depict the observations associated to a cluster of variables. We demonstrate our method on two small data sets and then use it to study a challenging genomic data set comprising >10,000 samples. We show that Association Plots can clearly highlight those observations which characterise a cluster of variables.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91226610","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

Longitudinal Canonical Correlation Analysis. 纵向典型相关分析。

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-06-01 DOI: 10.1093/jrsssc/qlad022

Seonjoo Lee, Jongwoo Choi, Zhiqian Fang, F DuBois Bowman

引用次数: 0

Outcome trajectory estimation for optimal dynamic treatment regimes with repeated measures. 重复测量最佳动态治疗方案的结果轨迹估计。

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-05-22 eCollection Date: 2023-08-01 DOI: 10.1093/jrsssc/qlad037

Yuan Zhang, David M Vock, Megan E Patrick, Lizbeth H Finestack, Thomas A Murray

引用次数: 0

Identifying irregular activity sequences: an application to passive household monitoring 识别不规则活动序列:在被动家庭监测中的应用

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-05-17 DOI: 10.1093/jrsssc/qlad005

Jess Gillam, R. Killick, Simon Taylor, Jack Heal, Ben Norwood

引用次数: 0

The impact of directly observed therapy on the efficacy of Tuberculosis treatment: a Bayesian multilevel approach 直接观察治疗对结核病治疗效果的影响:贝叶斯多水平方法

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-04-25 DOI: 10.1093/jrsssc/qlad034

Widemberg S. Nobre, A. M. Schmidt, E. Moodie, D. Stephens

引用次数: 0

A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data. 使用CyTOF数据识别细胞亚群的贝叶斯特征分配模型。

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-04-25 eCollection Date: 2023-06-01 DOI: 10.1093/jrsssc/qlad029

Arthur Lui, Juhee Lee, Peter F Thall, May Daher, Katy Rezvani, Rafet Basar

{"title":"A Bayesian feature allocation model for identifying cell subpopulations using CyTOF data.","authors":"Arthur Lui, Juhee Lee, Peter F Thall, May Daher, Katy Rezvani, Rafet Basar","doi":"10.1093/jrsssc/qlad029","DOIUrl":"10.1093/jrsssc/qlad029","url":null,"abstract":"<p><p>A Bayesian feature allocation model (FAM) is presented for identifying cell subpopulations based on multiple samples of cell surface or intracellular marker expression level data obtained by cytometry by time of flight (CyTOF). Cell subpopulations are characterized by differences in marker expression patterns, and cells are clustered into subpopulations based on their observed expression levels. A model-based method is used to construct cell clusters within each sample by modeling subpopulations as latent features, using a finite Indian buffet process. Non-ignorable missing data due to technical artifacts in mass cytometry instruments are accounted for by defining a static missingship mechanism. In contrast with conventional cell clustering methods, which cluster observed marker expression levels separately for each sample, the FAM-based method can be applied simultaneously to multiple samples, and also identify important cell subpopulations likely to be otherwise missed. The proposed FAM-based method is applied to jointly analyse three CyTOF datasets to study natural killer (NK) cells. Because the subpopulations identified by the FAM may define novel NK cell subsets, this statistical analysis may provide useful information about the biology of NK cells and their potential role in cancer immunotherapy which may lead, in turn, to development of improved NK cell therapies.</p>","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10264057/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9752620","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

Estimating subject-specific hazard functions 估计特定主题的危害函数

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-04-24 DOI: 10.1093/jrsssc/qlad030

Moumita Chatterjee, B. Ganguli, Sugata Sen Roy

引用次数: 0

Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data 在相关多重输入数据上进行变量选择的惩罚加权最小二乘估计

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-04-24 DOI: 10.1093/jrsssc/qlad028

Yang Li, Haoyu Yang, Haochen Yu, Hanwen Huang, Ye Shen

{"title":"Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data","authors":"Yang Li, Haoyu Yang, Haochen Yu, Hanwen Huang, Ye Shen","doi":"10.1093/jrsssc/qlad028","DOIUrl":"https://doi.org/10.1093/jrsssc/qlad028","url":null,"abstract":"\u0000 Considering the inevitable correlation among different datasets within the same subject, we propose a framework of variable selection on multiply imputed data with penalized weighted least squares (PWLS–MI). The methodological development is motivated by an epidemiological study of A/H7N9 patients from Zhejiang province in China, where nearly half of the variables are not fully observed. Multiple imputation is commonly adopted as a missing data processing method. However, it generates correlations among imputed values within the same subject across datasets. Recent work on variable selection for multiply imputed data does not fully address such similarities. We propose PWLS–MI to incorporate the correlation when performing the variable selection. PWLS–MI can be considered as a framework for variable selection on multiply imputed data since it allows various penalties. We use adaptive LASSO as an illustrating example. Extensive simulation studies are conducted to compare PWLS–MI with recently developed methods and the results suggest that the proposed approach outperforms in terms of both selection accuracy and deletion accuracy. PWLS–MI is shown to select variables with clinical relevance when applied to the A/H7N9 database.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2023-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91394783","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 spline-based time-varying reproduction number for modelling epidemiological outbreaks 基于样条的时变繁殖数，用于模拟流行病学暴发

IF 1.6 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-04-05 DOI: 10.1093/jrsssc/qlad027

Eugen Pircalabelu

引用次数: 2