An objective prior from a scoring rule
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2021-07-01
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Abstract
In this paper, we introduce a novel objective prior distribution levering on the connections between information, divergence and scoring rules. In particular, we do so from the starting point of convex functions representing information in density functions. This provides a natural route to proper local scoring rules using Bregman divergence. Specifically, we determine the prior which solves setting the score function to be a constant. Although in itself this provides motivation for an objective prior, the prior also minimizes a corresponding information criterion.
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Walker, SG, and C Villa (2021). An objective prior from a scoring rule. Entropy, 23(7). pp. 833–833. 10.3390/e23070833 Retrieved from https://hdl.handle.net/10161/33550.
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Cristiano Villa
Prof. Cristiano Villa main research area is in Bayesian statistics, with particular interest in objective methods. His output has been published in several peer-reviewed journals and presented at international conferences, such as the ISBA International Conference, the O-Bayes conference, and the ERCIM conference. In addition to his research, Prof. Villa is deeply committed to teaching and enjoys interacting with students. His teaching interests include probability, statistics, linear modelling, and risk management. Before joining Duke Kunshan University (DKU), Prof. Villa was a member of the Newcastle University (UK) and the University of Kent (UK). Prior to joining academia in 2014, he worked as an auditor and as an advisor for KPMG in several countries, including, Italy, UK, New Zealand, and Singapore. He holds an M.Sc. and a Ph.D. from the University of Kent, UK.
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