A0870
Title: From big-data to small area estimation: Statistical meta-analysis approach
Authors: Jeffrey Wilson - Arizona State University (United States)
Din Chen - University of Pretoria (South Africa) [presenting]
Abstract: In the era of big data deluge, meta-analytics offers unprecedented opportunities to enhance the precision and relevance of statistical inference, especially in public health and social sciences. Another critical area of meta-analytics is small area estimation (SAE), where reliable statistics are required at granular geographic or demographic levels, often with limited local data. The purpose is to explore the integration of big data analytics and SAE with meta-analytic frameworks. It begins by discussing the challenges in big-data sciences and SAE, and then by introducing a meta-analysis-based approach as a flexible tool for synthesizing diverse data sources. The methodological innovations, computational strategies, and practical implications for policy and planning are highlighted. This cross-disciplinary approach provides a blueprint for leveraging big data to inform local decision-making through statistically principled estimation.
