Grouped spatial autoregressive model

Huang, Danyang1,2; Hu, Wei1,2; Jing, Bingyi3; Zhang, Bo1,2,4

2023-02-01

10.1016/j.csda.2022.107601

COMPUTATIONAL STATISTICS & DATA ANALYSIS   影响因子和分区

0167-9473

1872-7352

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.

Conditional least -squares estimation Network autocorrelation heterogeneity Large-scale network Naive least -squares estimation Spatial autoregressive panel model

SCI ; EI

英语

其他

National Natural Science Foundation of China["12071477","11701560","71873137"]

Computer Science ; Mathematics

Computer Science, Interdisciplinary Applications ; Statistics & Probability

WOS:000853215400001

ELSEVIER

20223712729655

Least squares approximations ; Numerical methods

Mathematics:921 ; Numerical Methods:921.6

MATHEMATICS

Web of Science

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

Huang, Danyang,Hu, Wei,Jing, Bingyi,et al. Grouped spatial autoregressive model[J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS,2023,178.

Huang, Danyang,Hu, Wei,Jing, Bingyi,&Zhang, Bo.(2023).Grouped spatial autoregressive model.COMPUTATIONAL STATISTICS & DATA ANALYSIS,178.

Huang, Danyang,et al."Grouped spatial autoregressive model".COMPUTATIONAL STATISTICS & DATA ANALYSIS 178(2023).