The New Palgrave Dictionary of Economics

Living Edition
| Editors: Palgrave Macmillan

Maximum Likelihood

  • R. L. Basmann
Living reference work entry

Later version available View entry history

First Online:
Received:
Accepted:
DOI: https://doi.org/10.1057/978-1-349-95121-5_976-1
How to cite

Abstract

Maximum likelihood is primarily a strategy for measuring the relative empirical ‘support’ that an observed sample X of data affords rival statistical hypotheses and parameter estimates. A statistical model is described by a completely specified form of probability function or probability density function, f(x; θ),along with ranges for the observable random variable, x, and the parameter vector, θ (See the example (1d–e) below.) θ may contain more than one parameter element; for instance, if the parent density is the familiar normal density function, then θ = (μ, σ 2)

Keywords

Maximum Likelihood Estimator Inference Problem Initial Support Prior Density Parent Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Bibliography

  1. Berger, J., and Wolpert, R. 1984. The likelihood principle. Institute of Mathematical Statistics Monograph Series, Haywood, California.Google Scholar
  2. Bernoulli, D. 1777. Diiudicatio maxime probabilis plurium obferuationum difcrepantium atque verificillima inductio inde formanda. In Acta Academiae Scientiarum Imperialis Petropolitanae, Petropoli: Typis Academiae Scientiarum, 3–33. Reprinted 1961, with foreword by M.G. Kendall and observations by L. Euler in Biometrika 48: 1–18.Google Scholar
  3. Edgeworth, F. 1908. On the probable errors of frequency constants. Journal of the Royal Statistical Society 71(June): 381–97.CrossRefGoogle Scholar
  4. Edwards, A.W.F. 1972. Likelihood. Cambridge: Cambridge University Press.Google Scholar
  5. Fisher, R.A. 1912. On an absolute criterion for fitting frequency curves. Messenger of Mathematics 41: 155–60.Google Scholar
  6. Fisher, R.A. 1920. A mathematical examination of the methods of determining the accuracy of an observation by the mean error, and by the mean square error. Monthly Notices of the Royal Astronomical Society 80: 758–70.CrossRefGoogle Scholar
  7. Fisher, R.A. 1925. Theory of statistical estimation. Proceedings of the Cambridge Philosophical Society 22: 700–25.CrossRefGoogle Scholar
  8. Fisher, R.A. 1956. Statistical methods and scientific inference. Edinburgh/London: Oliver & Boyd.Google Scholar
  9. Gauss, C.F. 1823. Theoria combinationis observationum erroribus minimis obnoxiae. Gottingen: Apud Henricum Dieterich.Google Scholar
  10. Hacking, I. 1965. Logic of statistical inference. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  11. Jeffreys, H. 1983. Theory of probability, 3rd ed. Oxford: Clarendon.Google Scholar
  12. Todhunter, I. 1865. A history of the mathematical theory of probability. Cambridge/London: Macmillan.Google Scholar
  13. Zellner, A. 1984. Basic issues in econometrics. Chicago: University of Chicago Press.Google Scholar

Copyright information

© The Author(s) 1987

Authors and Affiliations

  • R. L. Basmann
    • 1
  1. 1.