2003 TutorialOnMLE

Jump to: navigation, search

Subject Headings: Maximum Likelihood Estimator, Least Squares Estimator.


Cited By



In this paper, I provide a tutorial exposition on maximum likelihood estimation (MLE). The intended audience of this tutorial are researchers who practice mathematical modeling of cognition but are unfamiliar with the estimation method. Unlike least-squares estimation which is primarily a descriptive tool, MLE is a preferred method of parameter estimation in statistics and is an indispensable tool for many statistical modeling techniques, in particular in non-linear modeling with non-normal data. The purpose of this paper is to provide a good conceptual explanation of the method with illustrative examples so the reader can have a grasp of some of the basic principles.


  • Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In: Petrox, B.N., & Caski, F. Second international symposium on information theory (pp. 267–281). Budapest: Akademiai Kiado.
  • Batchelder, W. H., & Crowther, C. S. (1997). Multinomial processing tree models of factorial categorization. Journal of Mathematical Psychology, 41, 45–55.
  • Bickel, P. J., & Doksum, K. A. (1977). Mathematical statistics. Oakland, CA: Holden-day, Inc. Casella, G., & Berger, R. L. (2002). Statistical inference (2nd ed.). Pacific Grove, CA: Duxberry. DeGroot, M. H., & Schervish, M. J. (2002). Probability and statistics (3rd ed.). Boston, MA: Addison-Wesley. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671–680.
  • Lamberts, K. (2000). Information-accumulation theory of speeded categorization. Psychological Review, 107(2), 227–260.
  • Linhart, H., & Zucchini, W. (1986). Model selection. New York, NY: Wiley.
  • MurdockJr., B. B. (1961). The retention of individual items. Journal of Experimental Psychology, 62, 618–625.
  • Myung, I. J., Forster, M., & Browne, M. W. (2000). Special issue on model selection. Journal of Mathematical Psychology, 44, 1–2.
  • Pitt, M. A., Myung, I. J., & Zhang, S. (2002). Toward a method of selecting among computational models of cognition. Psychological Review, 109, 472–491.
  • Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85, 59–108.
  • Rubin, D. C., Hinton, S., & Wenzel, A. (1999). The precise time course of retention. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 1161–1176.
  • Rubin, D. C., & Wenzel, A. E. (1996). One hundred years of forgetting: A quantitative description of retention. Psychological Review, 103, 734–760.
  • Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461–464.
  • Spanos, A. (1999). Probability theory and statistical inference. Cambridge, UK: Cambridge University Press.
  • Usher, M., & James L. McClelland (2001). The time course of perceptual choice; The leaky, competing accumulator model. Psychological Review, 108(3), 550–592.
  • Van Zandt, T. (2000). How to fit a response time distribution. Psychonomic Bulletin & Review, 7(3), 424–465.
  • Wickens, T. D. (1998). On the form of the retention function: Comment on Rubin and Wenzel (1996): A quantitative description of retention. Psychological Review, 105, 379–386.
  • Wixted, J. T., & Ebbesen, E. B. (1991). On the form of forgetting. Psychological Science, 2, 409–415.


 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2003 TutorialOnMLEIn Jae MyungTutorial on Maximum Likelihood EstimationJournal of Mathematical Psychologyhttp://faculty.psy.ohio-state.edu/myung/personal/mle.pdf10.1016/S0022-2496(02)00028-7