PSEUDO-LIKELIHOOD INFERENCE UNDERESTIMATES MODEL UNCERTAINTY: EVIDENCE FROM BAYESIAN NEAREST NEIGHBOURS

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WANHUA SU; CHIPMAN HUGH; ZHU MU

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Paper Information

Journal: JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS) Year:2011 | Volume:10 | Issue:2 Page(s): 167-180

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Information Journal Paper

Title

PSEUDO-LIKELIHOOD INFERENCE UNDERESTIMATES MODEL UNCERTAINTY: EVIDENCE FROM BAYESIAN NEAREST NEIGHBOURS

Author(s)

WANHUA SU | CHIPMAN HUGH | ZHU MU | Issue Writer Certificate

Keywords

BOOTSTRAP INTERVALQ4

MCMCQ4

POSTERIOR INTERVALQ4

PSEUDO-LIKELIHOODQ4

Abstract

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.

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APA: Copy

WANHUA, SU, CHIPMAN, HUGH, & ZHU, MU. (2011). PSEUDO-LIKELIHOOD INFERENCE UNDERESTIMATES MODEL UNCERTAINTY: EVIDENCE FROM BAYESIAN NEAREST NEIGHBOURS. JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS), 10(2), 167-180. SID. https://sid.ir/paper/117832/en

Vancouver: Copy

WANHUA SU, CHIPMAN HUGH, ZHU MU. PSEUDO-LIKELIHOOD INFERENCE UNDERESTIMATES MODEL UNCERTAINTY: EVIDENCE FROM BAYESIAN NEAREST NEIGHBOURS. JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS)[Internet]. 2011;10(2):167-180. Available from: https://sid.ir/paper/117832/en

IEEE: Copy

SU WANHUA, HUGH CHIPMAN, and MU ZHU, “PSEUDO-LIKELIHOOD INFERENCE UNDERESTIMATES MODEL UNCERTAINTY: EVIDENCE FROM BAYESIAN NEAREST NEIGHBOURS,” JOURNAL OF THE IRANIAN STATISTICAL SOCIETY (JIRSS), vol. 10, no. 2, pp. 167–180, 2011, [Online]. Available: https://sid.ir/paper/117832/en

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