| Title | Grouped spatial autoregressive model |
| Author | |
| Corresponding Author | Hu, Wei; Zhang, Bo |
| Publication Years | 2023-02-01
|
| DOI | |
| Source Title | |
| ISSN | 0167-9473
|
| EISSN | 1872-7352
|
| Volume | 178 |
| Abstract | With the development of the internet, network data with replications can be collected at different time points. The spatial autoregressive panel (SARP) model is a useful tool for analyzing such network data. However, in the traditional SARP model, all individuals are assumed to be homogeneous in their network autocorrelation coefficients, while in practice, correlations could differ for the nodes in different groups. Here, a grouped spatial autoregressive (GSAR) model based on the SARP model is proposed to permit network autocorrelation heterogeneity among individuals, while analyzing network data with independent replications across different time points and strong spatial effects. Each individual in the network belongs to a latent specific group, which is characterized by a set of parameters. Two estimation methods are studied: two-step naive least-squares estimator, and two-step conditional least-squares estimator. Furthermore, their corresponding asymptotic properties and technical conditions are investigated. To demonstrate the performance of the proposed GSAR model and its corresponding estimation methods, numerical analysis was performed on simulated and real data.(c) 2022 Elsevier B.V. All rights reserved. |
| Keywords | |
| URL | [Source Record] |
| Indexed By | |
| Language | English
|
| SUSTech Authorship | Others
|
| Funding Project | National Natural Science Foundation of China["12071477","11701560","71873137"]
|
| WOS Research Area | Computer Science
; Mathematics
|
| WOS Subject | Computer Science, Interdisciplinary Applications
; Statistics & Probability
|
| WOS Accession No | WOS:000853215400001
|
| Publisher | |
| EI Accession Number | 20223712729655
|
| EI Keywords | Least squares approximations
; Numerical methods
|
| ESI Classification Code | Mathematics:921
; Numerical Methods:921.6
|
| ESI Research Field | MATHEMATICS
|
| Data Source | Web of Science
|
| Citation statistics |
Cited Times [WOS]:0
|
| Document Type | Journal Article |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/401500 |
| Department | Department of Statistics and Data Science |
| Affiliation | 1.Renmin Univ China, Ctr Appl Stat, Beijing, Peoples R China 2.Renmin Univ China, Sch Stat, Beijing, Peoples R China 3.Southern Univ Sci & Technol, Dept Stat & Data Sci, Shenzhen, Peoples R China 4.Renmin Univ China, Beijing 100872, Peoples R China |
| Recommended Citation GB/T 7714 |
Huang, Danyang,Hu, Wei,Jing, Bingyi,et al. Grouped spatial autoregressive model[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS,2023,178.
|
| APA |
Huang, Danyang,Hu, Wei,Jing, Bingyi,&Zhang, Bo.(2023).Grouped spatial autoregressive model.COMPUTATIONAL STATISTICS & DATA ANALYSIS,178.
|
| MLA |
Huang, Danyang,et al."Grouped spatial autoregressive model".COMPUTATIONAL STATISTICS & DATA ANALYSIS 178(2023).
|
| Files in This Item: | There are no files associated with this item. | |||||
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