Newton’s Method and Scoring

  • Kenneth Lange
Part of the Statistics for Biology and Health book series (SBH)


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.


Exponential Family Multinomial Distribution Dirichlet Distribution Observe Information Matrix Tandem Repeat Locus 
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

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Kenneth Lange
    • 1
  1. 1.Department of Biostatistics and MathematicsUniversity of MichiganAnn ArborUSA

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