Statistical Generalization Paradigms for Establishing New Foundations in Engineering Informatics Research
Keywords:
Engineering informatics, statistical generalization, model uncertainty, multi-level inference, cross-domain validation, reproducibility, epistemic uncertainty, informatics frameworkAbstract
Engineering informatics, an interdisciplinary field that blends computational, statistical, and domain-specific knowledge, is rapidly evolving. Despite advances in computational methods and data analytics, challenges persist in ensuring the statistical generalizability of research findings. This paper proposes new paradigms for statistical generalization within engineering informatics, emphasizing replicability, interpretability, and cross-contextual validation. By integrating methodologies from statistical learning theory, model uncertainty quantification, and multi-level inference, this work advocates for a shift in how generalizability is conceptualized and achieved.
References
Gero JS, Kannengiesser U. The situated function–behavior–structure framework. Design Studies 25, 373–391 (2004)
Forsberg K, Mooz H. The relationship of system engineering to the project cycle. Engineering Management Journal 3, 36–43 (1991)
Eastman CM. Building product models: computer environments supporting design and construction. CRC Press (1999)
Chen P, Doshi P. Ontology-driven information integration in engineering applications. Advanced Engineering Informatics 20, 31–45 (2006)
Bouchlaghem D, Shang H, Whyte J, Ganah A. Visualisation in architecture, engineering and construction (AEC). Automation in Construction 14, 287–295 (2005)
Karimi IA, Namjoshi O. Automated generation of simulation models using rule-based systems. Computers and Chemical Engineering 34, 1154–1163 (2010)
Turk Ž. Ten questions concerning building information modelling. Building and Environment 107, 274–284 (2016)
Amor R, Jiang Y, Chen X. BIM and model checking. Automation in Construction 84, 163–175 (2017)
Zhang Y, El-Gohary N. Ontology-based framework for information extraction from infrastructure documents. Advanced Engineering Informatics 36, 160–173 (2018)
Breiman L. Statistical modeling: The two cultures. Statistical Science 16, 199–231 (2001)
Gelman A, Hill J. Data analysis using regression and multilevel/hierarchical models. Cambridge University Press (2007)
Efron B, Hastie T. Computer age statistical inference. Cambridge University Press (2016)
Shmueli G. To explain or to predict? Statistical Science 25, 289–310 (2010)
Bishop CM. Pattern recognition and machine learning. Springer (2006)
Burnham KP, Anderson DR. Model selection and multimodel inference. Springer (2002)
Pearl J. Causality: Models, reasoning and inference. Cambridge University Press (2000)
