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On Bayesian thresholding and truncation methods
Journal of the Korean Data & Information Science Society 2022;33:927-36
Published online September 30, 2022;
© 2022 Korean Data and Information Science Society.

Byungwon Kim1 · Yeongwoo Park2 · Yongku Kim3

13Department of Statistics, Kyungpook National University 2National Health Insurance Service
Correspondence to: This research was supported by the Research Grants of Korea Forest Service (Korea Forestry Promotion Institute) project (No.2019149B10-2223-0301).
1 Assistant professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea
2 Researcher, National Health Insurance Service, Wonju 26464, Korea
3 Professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail:
Received June 18, 2022; Revised July 3, 2022; Accepted July 5, 2022.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Thresholding dynamic process is a powerful statistical tool for modeling structural nonlinear relationships. We here discuss a Bayesian formalism to give rise to a type of threshold estimation in dynamic process with spatial structure. A prior distribution is imposed on the unknown parameter of process model, designed to capture the sparseness of process parameters that is common to most application. For the prior specified, the posterior distribution yields a thresholding procedure. In this paper, we introduce a general approach in which the truncation step is directly implanted to MCMC procedure by thresholding the MCMC outputs. The proposed thresholding approach is applied to the basal topography of the Northeast Ice-Stream in Greenland by using Daubechies wavelet-based analysis.
Keywords : Bayesian analysis, dynamic process model, Markov chain Monte carlo, thresholding, truncation.