Abstract
This chapter explores some alternatives to maximum likelihood estimation by the EM algorithm. Newton’s method and scoring usually converge faster than the EM algorithm. However, the trade-offs of programming ease, numerical stability, and speed of convergence are complex, and statistical geneticists should be fluent in a variety of numerical optimization techniques for finding maximum likelihood estimates. Outside the realm of maximum likelihood, Bayesian procedures have much to offer in small to moderate-sized problems. For those uncomfortable with pulling prior distributions out of thin air, empirical Bayes procedures can be an appealing compromise between classical and Bayesian methods. This chapter illustrates some of these well-known themes in the context of allele frequency estimation and linkage analysis.
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© 2002 Springer-Verlag New York, Inc.
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Lange, K. (2002). Newton’s Method and Scoring. In: Mathematical and Statistical Methods for Genetic Analysis. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-21750-5_3
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DOI: https://doi.org/10.1007/978-0-387-21750-5_3
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4684-9556-0
Online ISBN: 978-0-387-21750-5
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