Development and Evaluation of Statistical Models for Network Data, Such as Social Networks, Biological Networks and Brain Networks in Vietnam - Phuong, Hoang (Journal of Statistics and Actuarial Research, 2024)
Purpose: The aim of the study was to investigate development and evaluation of statistical models for network data, such as social networks, biological networks, and brain networks Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Statistical models for network data, encompassing social, biological, and brain networks, have enhanced our comprehension of these complex systems. These models reveal structural insights, dynamic patterns, and valuable applications across multiple disciplines, including epidemiology, genetics, and neuroscience. In essence, they provide indispensable tools for understanding intricate network dynamics. Unique Contribution to Theory, Practice and Policy: Principal component analysis (PCA), Deep learning and neural networks & Information theory may be used to anchor future studies on development and evaluation of statistical models for network data, such as social networks, biological networks, and brain networks. Create user-friendly software tools and packages for implementing advanced survival models, making them accessible to researchers and practitioners. By identifying trends and disparities over time, policymakers can design interventions and allocate resources more effectively.