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Alternative Nonparametrics

  • Bertrand Clarke
  • Ernest Fokoué
  • Hao Helen Zhang
Chapter
Part of the Springer Series in Statistics book series (SSS)

Having seen Early, Classical and New Wave nonparametrics, along with partitioningbased classification methods, it is time to examine the most recently emerging class of techniques, here called Alternative methods in parallel with contemporary music. The common feature all these methods have is that they are more abstract. Indeed, the four topics covered here are abstract in different ways. Model-averaging methods usually defy interpretability. Bayesian nonparametrics requires practitioners to think carefully about the space of functions being assumed in order to assign a prior. The relevance vector machine (RVM) a competitor to support vector machines, tries to obtain sparsity by using asymptotic normality; again the interpretability is mostly lost. Hidden Markov models pre-suppose an unseen space to which all the estimates are referred. The ways in which these methods are abstract vary, but it is hard to dispute that the degree of abstraction they require exceeds that of the earlier methods.

Keywords

Hide Markov Model Random Forest Relative Entropy Ensemble Method Dirichlet Process 
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-Verlag New York 2009

Authors and Affiliations

  • Bertrand Clarke
    • 1
  • Ernest Fokoué
    • 2
  • Hao Helen Zhang
    • 3
  1. 1.University of MiamiMiamiCanada
  2. 2.Department of Science & MathematicsKettering UniversityFlintUSA
  3. 3.Department of StatisticsNorth Carolina State University Program in Statistical GeneticsRaleighUSA

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