Volume 10, Issue 2 (November 2011)                   JIRSS 2011, 10(2): 167-180 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Su W, Chipman H, Zhu M. Pseudo-Likelihood Inference Underestimates Model Uncertainty: Evidence from Bayesian Nearest Neighbours. JIRSS. 2011; 10 (2) :167-180
URL: http://jirss.irstat.ir/article-1-162-en.html
Abstract:   (7866 Views)
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.
Full-Text [PDF 166 kb]   (3016 Downloads)    

Received: 2011/11/7 | Accepted: 2015/09/12 | Published: 2011/11/15

© 2015 All Rights Reserved | Journal of The Iranian Statistical Society