Assessing the Relationship Between Model Selection Criteria and Predictive Performance in Data-Driven Research
Posted: Jul 31, 2009
Abstract
This research addresses a critical gap in the methodological literature by systematically examining the relationship between model selection criteria and actual predictive performance. While numerous studies have investigated the theoretical properties of individual criteria, comprehensive empirical assessments across varied data conditions are surprisingly limited. The complexity of this relationship is heightened by the increasing diversity of data structures encountered in contemporary research, including high-dimensional datasets, complex dependency structures, and heterogeneous data generating processes. Understanding how selection criteria perform across these varied contexts is essential for advancing methodological best practices and ensuring the integrity of data-driven scientific conclusions.
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