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The Method of Maximum Likelihood

In: Fundamentals of Statistical Inference

Author

Listed:
  • Konstantin M. Zuev

    (California Institute of Technology, Department of Computing and Mathematical Sciences)

Abstract

Maximum likelihood estimation is one of the most popular methods for estimating parameters in parametric models. It was introduced, studied, and popularized by Ronald Fisher, one of the greatest statisticians of all time. Maximum likelihood estimates are known to be very powerful and have many attractive properties, especially when the sample size is large. In this chapter, we will provide the intuition behind this method, define the likelihood function and the maximum likelihood estimate, consider several classical examples, and discuss the main properties of the method.

Suggested Citation

  • Konstantin M. Zuev, 2026. "The Method of Maximum Likelihood," International Series in Operations Research & Management Science, in: Fundamentals of Statistical Inference, chapter 8, pages 145-183, Springer.
    • Handle: RePEc:spr:isochp:978-3-032-03848-7_8
    DOI: 10.1007/978-3-032-03848-7_8
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