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:   (6652 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]   (2519 Downloads)    
Subject: 60: Probability theory and stochastic processes
Received: 2011/11/7 | Accepted: 2015/09/12

Add your comments about this article : Your username or Email:

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