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Detection of multiple change points for non-stationary network autoregressive models.

Abstract Number:

1930 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Ruishan Lin (1), Abolfazl Safikhani (2)

Institutions:

(1) N/A, N/A, (2) University of Florida, N/A

Co-Author:

Abolfazl Safikhani  
University of Florida

First Author:

Ruishan Lin  
N/A

Presenting Author:

Ruishan Lin  
N/A

Abstract Text:

The Network Autoregression (NAR) model is widely used for analyzing network-dependent data. However, assuming fixed parameters over time is often unrealistic in dynamic systems. Identifying time points where NAR parameters shift-known as change points-is crucial for capturing structural changes in the network process. This work proposes a rolling-window approach to detect these change points efficiently. The method adapts to evolving network structures and parameter variations, improving the model's flexibility in real-world applications. Simulation studies demonstrate the effectiveness of the proposed approach, and its applicability is further illustrated using real-world network data.

Keywords:

Change Point Detection,| Network Autoregressive (NAR) Model |Network-Dependent Data|Non-Stationary Processes|Structural Changes|

Sponsors:

IMS

Tracks:

Statistical Methodology

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