Alternative Nonparametrics

Abstract

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.