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Maximum Likelihood Estimation

  • Roy Robinson

Abstract

It is possible to develop a number of systems of estimation and nowhere does this seem to be more true than for the estimation of genetic crossover fractions. Several of these fail dismally because of inaccuracy and inefficiency (Fisher and Balmukand, 1928). Others have received consideration in the main because of their interesting and special features. These exceptional systems are known as minimum χ2, product ratio, and minimum discrepancy (Fisher and Balmukand, 1928; Immer, 1930; Haldance,1953; Murty, 1954b). The most versatile and efficient system, however, is the method of maximum likelihood (Fisher, 1922; Mather, 1951; Bailey, 1961). The main objection to the method, namely, that the estimating formulae are often of high degree polynomials, is only correct for a few cases and in the event can be side-stepped by the use of scores.

Keywords

Maximum Likelihood Estimation Single Parameter Information Matrix Estimate Formula Single Observation 
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.

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Copyright information

© Plenum Publishing Company Ltd. 1971

Authors and Affiliations

  • Roy Robinson

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