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. (2022). Pseudo-Likelihood Inference Underestimates Model Uncertainty: Evidence from Bayesian Nearest Neighbours. Journal of the Iranian Statistical Society, 10(2), 167-180.
MLA
Su, W., Chipman, H., & Zhu, M. "Pseudo-Likelihood Inference Underestimates Model Uncertainty: Evidence from Bayesian Nearest Neighbours", Journal of the Iranian Statistical Society, 10, 2, 2022, 167-180.
HARVARD
Su W., Chipman H., Zhu M. (2022). 'Pseudo-Likelihood Inference Underestimates Model Uncertainty: Evidence from Bayesian Nearest Neighbours', Journal of the Iranian Statistical Society, 10(2), pp. 167-180.
CHICAGO
W. Su, H. Chipman & M. Zhu, "Pseudo-Likelihood Inference Underestimates Model Uncertainty: Evidence from Bayesian Nearest Neighbours," Journal of the Iranian Statistical Society, 10 2 (2022): 167-180,
VANCOUVER
Su W., Chipman H., Zhu M. Pseudo-Likelihood Inference Underestimates Model Uncertainty: Evidence from Bayesian Nearest Neighbours. JIRSS. 2022;10(2):167-180.
