Pseudo-likelihood inference underestimates model uncertainty: evidence from bayesian nearest neighbours
Author
Faculty Advisor
Date
2011
Keywords
bootstrap interval, MCMC, posterior interval, pseudolikelihood
Abstract (summary)
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.
Publication Information
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
DOI
Notes
Item Type
Article
Language
English
Rights
All Rights Reserved