This paper proposes an extended hierarchical hyperprior structure for kernel regression with the finality of solving the so-called Neyman-Scott problem inherent in the now very popular Relevance Vector Machine (RVM). We conjecture that the proposed prior helps achieve consistent estimates of the quantities of interest, thereby overcoming a limitation of the original RVM for which the estimates of the quantities of interest are shown to be inconsistent. Unlike the majority of other authors interested who typically used an Empirical Bayes approach for RVM, we adopt a fully Bayesian approach. Our consistency claim at this stage remains only a conjecture, to be proved theoretically in a subsequent paper. However, we use a computational argument to demonstrate the merits of proposed solution.
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The John D. Hromi Center for Quality and Applied Statistics (KGCOE)
Fokoue, Ernest; Sun, Dongchu; and Goel, Prem, "Fully Bayesian analysis of the relevance vector machine with consistency inducing priors" (2009). Elsevier,Accessed from
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