文献详情
Integrating approximate single factor graphical models
文献类型：期刊
期刊名称：Statistics in medicine影响因子和分区
年：2020
卷：39
期：2
页码：146-155
ISSN：1097-0258
关键词：approximate single factor graphical model,integrative analysis,penalized high dimensional analysis
所属部门：统计学院
摘要：In the analysis of complex and high-dimensional data, graphical models have been commonly adopted to describe associations among variables. When common factors exist which make the associations dense, the single factor graphical model has been proposed, which first extracts the common factor and then conducts graphical modeling. Under other simpler contexts, it has been recognized that results generated from analyzing a single dataset are often unsatisfactory, and integrating multiple datasets c ...More
In the analysis of complex and high-dimensional data, graphical models have been commonly adopted to describe associations among variables. When common factors exist which make the associations dense, the single factor graphical model has been proposed, which first extracts the common factor and then conducts graphical modeling. Under other simpler contexts, it has been recognized that results generated from analyzing a single dataset are often unsatisfactory, and integrating multiple datasets can effectively improve variable selection and estimation. In graphical modeling, the increased number of parameters makes the "lack of information" problem more severe. In this article, we integrate multiple datasets and conduct the approximate single factor graphical model analysis. A novel penalization approach is developed for the identification and estimation of important loadings and edges. An effective computational algorithm is developed. A wide spectrum of simulations and the analysis of breast cancer gene expression datasets demonstrate the competitive performance of the proposed approach. Overall, this study provides an effective new venue for taking advantage of multiple datasets and improving graphical model analysis.? 2019 John Wiley & Sons, Ltd. ...Hide

DOI：10.1002/sim.8408
百度学术：Integrating approximate single factor graphical models
语言：外文
作者其他论文



Identification of cancer omics commonality and difference via community fusion.Sun Yifan, Jiang Yu, Li Yang, et al. .Statistics in medicine. 2018.
Medical expenditure for middle-aged and elderly in Beijing.Ma Chenjin, Jiang Yan, Li Yang, et al. .BMC health services research. 2019, 19(1), 360.
Penalized integrative semiparametric interaction analysis for multiple genetic datasets.Li Yang, Li Rong, Lin Cunjie, et al. .Statistics in medicine. 2019.
Identification of cancer omics commonality and difference via community fusion.Sun Yifan, Jiang Yu, Li Yang, et al. .Statistics in medicine. 2019, 38(7), 1200-1212.
An integrative sparse boosting analysis of cancer genomic commonality and difference.Sun Yifan, Sun Zhengyang, Jiang Yu, et al. .Statistical methods in medical research. 2019, 962280219859026.