Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-15T03:51:59.288Z Has data issue: false hasContentIssue false
  • English
  • Français

Increasing generalizability via the principle of minimum description length

Published online by Cambridge University Press:  10 February 2022

Wes Bonifay Open the ORCID record for Wes Bonifay [Opens in a new window]
Wes Bonifay*
Affiliation:
Missouri Prevention Science Institute, University of Missouri, Columbia, MO65211, USAbonifayw@missouri.eduhttps://education.missouri.edu/person/wes-bonifay/
Get access Rights & Permissions [Opens in a new window]

Abstract

Traditional statistical model evaluation typically relies on goodness-of-fit testing and quantifying model complexity by counting parameters. Both of these practices may result in overfitting and have thereby contributed to the generalizability crisis. The information-theoretic principle of minimum description length addresses both of these concerns by filtering noise from the observed data and consequently increasing generalizability to unseen data.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 45 , 2022 , e5
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Petrov, B. N., & Csaki, F. (Eds.), Second international symposium on information theory (pp. 267–281). Budapest: Akademiai Kiado.Google Scholar
Bonifay, W., & Cai, L. (2017). On the complexity of item response theory models. Multivariate Behavioral Research, 52(4), 465–484.CrossRefGoogle ScholarPubMed
Cutting, J. E., Bruno, N., Brady, N. P., & Moore, C. (1992). Selectivity, scope, and simplicity of models: A lesson from fitting judgments of perceived depth. Journal of Experimental Psychology: General, 121(3), 364–381.CrossRefGoogle ScholarPubMed
Falk, C. F., & Muthukrishna, M. (2021). Parsimony in model selection: Tools for assessing fit propensity. Psychological Methods. Advance online publication. https://doi.org/10.1037/met0000422.CrossRefGoogle ScholarPubMed
Grünwald, P. (2005). A tutorial introduction to the minimum description length principle. In Grünwald, P., Myung, I.J., and Pitt, M. (Eds.), Advances in minimum description length: Theory and applications (pp. 3–81). MIT Press.CrossRefGoogle Scholar
Lakatos, I. (1978). Introduction: Science and pseudoscience. In Worrall, J. & Currie, G. (Eds.), The methodology of scientific research programs (pp. 1–8). Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Meehl, P. (1997). The problem is epistemology, not statistics: Replace significance tests by confidence intervals and quantify accuracy of risky numerical predictions. In Harlow, L. L., Mulaik, S. A. & Steiger, J. H. (Eds.), What if there were no significance tests? (pp. 393–425). Erlbaum.Google Scholar
Meehl, P. E. (1990). Appraising and amending theories: The strategy of lakatosian defense and two principles that warrant it. Psychological Inquiry, 1(2), 108–141.CrossRefGoogle Scholar
Myung, I. J., Pitt, M. A., & Kim, W. (2004). Model evaluation, testing and selection. In Lambert, K. & Goldstone, R. (Eds.), The handbook of cognition (pp. 422–436). Thousand Oaks, CA: Sage.Google Scholar
Myung, J. I., Navarro, D. J., & Pitt, M. A. (2006). Model selection by normalized maximum likelihood. Journal of Mathematical Psychology, 50(2), 167–179.CrossRefGoogle Scholar
Navarro, D. J. (2004). A note on the applied use of MDL approximations. Neural Computation, 16(9), 1763–1768.CrossRefGoogle ScholarPubMed
Piantadosi, S. T. (2018). One parameter is always enough. AIP Advances, 8(9), 095118.CrossRefGoogle Scholar
Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109(3), 472–491.CrossRefGoogle Scholar
Preacher, K. J. (2006). Quantifying parsimony in structural equation modeling. Multivariate Behavioral Research, 41(3), 227–259.CrossRefGoogle ScholarPubMed
Rissanen, J. (1978). Modeling by the shortest data description. Automatica, 14, 465–471.CrossRefGoogle Scholar
Rissanen, J. (1989). Stochastic complexity in statistical inquiry. Singapore: World Scientific Publishing.Google Scholar
Roberts, S., & Pashler, H. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358–367.CrossRefGoogle Scholar
Vitányi, P. M., & Li, M. (2000). Minimum description length induction, Bayesianism, and Kolmogorov complexity. IEEE Transactions on Information Theory, 46(2), 446–464.CrossRefGoogle Scholar
Watts, A. L., Poore, H. E., & Waldman, I. D. (2019). Riskier tests of the validity of the bifactor model of psychopathology. Clinical Psychological Science, 7(6), 1285–1303.CrossRefGoogle Scholar