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Modern estimation methods

  • E.W. Steyerberg
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Background

In this chapter we discuss methods to estimate biased regression coefficients, which lead to better predictions than those obtained with traditional methods. These modern estimation methods include uniform shrinkage methods (heuristic or bootstrap based) and penalized maximum likelihood methods (with various forms of penalty, including the “Lasso”). We illustrate the application of these methods with a data set of 785 patients from the GUSTO-I case study. It appears that rather advanced procedures can now readily be performed with modern software.

Keywords

Bootstrap Sample Linear Predictor Penalty Factor Estimate Regression Coefficient Shrinkage Factor 
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.

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • E.W. Steyerberg
    • 1
  1. 1.Department of Public HealthErasmus MCRotterdamThe Netherlands

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