| dc.contributor | 心理系 | |
| dc.creator (作者) | 黃柏僩 | |
| dc.creator (作者) | Huang, Po-Hsien | |
| dc.date (日期) | 2020-04 | |
| dc.date.accessioned | 10-Aug-2021 16:44:37 (UTC+8) | - |
| dc.date.available | 10-Aug-2021 16:44:37 (UTC+8) | - |
| dc.date.issued (上傳時間) | 10-Aug-2021 16:44:37 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/136774 | - |
| dc.description.abstract (摘要) | Sparse estimation via penalized likelihood (PL) is now a popular approach to learn the associations among a large set of variables. This paper describes an R package called lslx that implements PL methods for semi-confirmatory structural equation modeling (SEM). In this semi-confirmatory approach, each model parameter can be specified as free/fixed for theory testing, or penalized for exploration. By incorporating either a L1 or minimax concave penalty, the sparsity pattern of the parameter matrix can be efficiently explored. Package lslx minimizes the PL criterion through a quasi-Newton method. The algorithm conducts line search and checks the first-order condition in each iteration to ensure the optimality of the obtained solution. A numerical comparison between competing packages shows that lslx can reliably find PL estimates with the least time. The current package also supports other advanced functionalities, including a two-stage method with auxiliary variables for missing data handling and a reparameterized multi-group SEM to explore population heterogeneity. | |
| dc.format.extent | 692366 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Journal of Statistical Software, Vol.93, No.7, pp.1851 | |
| dc.title (題名) | lslx: Semi-Confirmatory Structural Equation Modeling via Penalized Likelihood | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.18637/jss.v093.i07 | |
| dc.doi.uri (DOI) | https://doi.org/10.18637/jss.v093.i07 | |
