Open Access
November 2007 The Epic Story of Maximum Likelihood
Stephen M. Stigler
Statist. Sci. 22(4): 598-620 (November 2007). DOI: 10.1214/07-STS249


At a superficial level, the idea of maximum likelihood must be prehistoric: early hunters and gatherers may not have used the words “method of maximum likelihood” to describe their choice of where and how to hunt and gather, but it is hard to believe they would have been surprised if their method had been described in those terms. It seems a simple, even unassailable idea: Who would rise to argue in favor of a method of minimum likelihood, or even mediocre likelihood? And yet the mathematical history of the topic shows this “simple idea” is really anything but simple. Joseph Louis Lagrange, Daniel Bernoulli, Leonard Euler, Pierre Simon Laplace and Carl Friedrich Gauss are only some of those who explored the topic, not always in ways we would sanction today. In this article, that history is reviewed from back well before Fisher to the time of Lucien Le Cam’s dissertation. In the process Fisher’s unpublished 1930 characterization of conditions for the consistency and efficiency of maximum likelihood estimates is presented, and the mathematical basis of his three proofs discussed. In particular, Fisher’s derivation of the information inequality is seen to be derived from his work on the analysis of variance, and his later approach via estimating functions was derived from Euler’s Relation for homogeneous functions. The reaction to Fisher’s work is reviewed, and some lessons drawn.


Download Citation

Stephen M. Stigler. "The Epic Story of Maximum Likelihood." Statist. Sci. 22 (4) 598 - 620, November 2007.


Published: November 2007
First available in Project Euclid: 7 April 2008

zbMATH: 1246.01016
MathSciNet: MR2410255
Digital Object Identifier: 10.1214/07-STS249

Keywords: Abraham Wald , efficiency , Harold Hotelling , History of statistics , Jerzy Neyman , karl Pearson , maximum likelihood , R. A. Fisher , sufficiency , superefficiency

Rights: Copyright © 2007 Institute of Mathematical Statistics

Vol.22 • No. 4 • November 2007
Back to Top