KMS Of Academy of mathematics and systems sciences, CAS
| Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach | |
Wu, Leqin1; Qiu, Xing2; Yuan, Ya-xiang3 ; Wu, Hulin4
| |
| 2019-04-03 | |
| Source Publication | JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
 |
| ISSN | 0162-1459 |
| Volume | 114Issue:526Pages:657-667 |
| Abstract | Ordinary differential equations (ODEs) are widely used to model the dynamic behavior of a complex system. Parameter estimation and variable selection for a "Big System" with linear ODEs are very challenging due to the need of nonlinear optimization in an ultra-high dimensional parameter space. In this article, we develop a parameter estimation and variable selection method based on the ideas of similarity transformation and separable least squares (SLS). Simulation studies demonstrate that the proposed matrix-based SLS method could be used to estimate the coefficient matrix more accurately and perform variable selection for a linear ODE system with thousands of dimensions and millions of parameters much better than the direct least squares method and the vector-based two-stage method that are currently available. We applied this new method to two real datasets-a yeast cell cycle gene expression dataset with 30 dimensions and 930 unknown parameters and the Standard & Poor 1500 index stock price data with 1250 dimensions and 1,563,750 unknown parameters-to illustrate the utility and numerical performance of the proposed parameter estimation and variable selection method for big systems in practice. Supplementary materials for this article are available online. |
| Keyword | Complex system Eigenvalue updating algorithm High dimension Matrix-based variable selection Ordinary differential equation Separable least squares |
| DOI | 10.1080/01621459.2017.1423074 |
| Language | 英语 |
| Funding Project | NIH[RO1 AI087135] ; Respiratory Pathogens Research Center (NIAID)[HHSN272201200005C] ; University of Rochester CTSA award from National Center for Advancing Translational Sciences of the National Institutes of Health[UL1 TR002001] ; University of Rochester Center for AIDS Research[NIH 5 P30 AI078498-08] ; National Natural Science Foundation of China[11526096] ; National Natural Science Foundation of China[11601185] |
| WOS Research Area | Mathematics |
| WOS Subject | Statistics & Probability |
| WOS ID | WOS:000472559400013 |
| Publisher | AMER STATISTICAL ASSOC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/34944 |
| Collection | 计算数学与科学工程计算研究所 |
| Corresponding Author | Wu, Hulin |
| Affiliation | 1.Jinan Univ, Dep Math, Guangzhou, Guangdong, Peoples R China 2.Univ Rochester, Dept Biostat & Computat Biol, Rochester, NY USA 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 4.Univ Texas Hlth Sci Ctr Houston, Dept Biostat, Houston, TX 77030 USA |
| Recommended Citation GB/T 7714 | Wu, Leqin,Qiu, Xing,Yuan, Ya-xiang,et al. Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,2019,114(526):657-667. |
| APA | Wu, Leqin,Qiu, Xing,Yuan, Ya-xiang,&Wu, Hulin.(2019).Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach.JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,114(526),657-667. |
| MLA | Wu, Leqin,et al."Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach".JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 114.526(2019):657-667. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment