Pseudo-likelihood inference underestimates model uncertainty: evidence from bayesian nearest neighbours
bootstrap interval, MCMC, posterior interval, pseudolikelihood
When using the K-nearest neighbours (KNN) method, one often ignores the uncertainty in the choice of K. To account for such uncertainty, Bayesian KNN (BKNN) has been proposed and studied (Holmes and Adams 2002 Cucala et al. 2009). We present some evidence to show that the pseudo-likelihood approach for BKNN, even after being corrected by Cucala et al. (2009), still significantly underestimates model uncertainty.
Su, W., Chipman, H., & Zhu, M. (2011). Pseudo-likelihood inference underestimates model uncertainty: evidence from bayesian nearest neighbours. Journal of the Iranian Statistical Society, 2(2), 167-180. http://jirss.irstat.ir/article-1-162-en.html
All Rights Reserved