Asymptotic Properties of Monte Carlo Strategies for a Cumulative Link Model

• Kamatani Kengo

  Graduate School of Engineering Science, Osaka University

Abstract

For a cumulative link model in the Bayesian context, the posterior distribution cannot be obtained in closed form, and we have to resort to an approximation method. A simple data-augmentation strategy is widely used for that purpose but is known to work poorly. The marginal augmentation procedure and the parameter-expanded data-augmentation procedure are considered to be remedies, but such strategies are still not free from poor convergence. In this paper, we propose a kind of the hybrid Markov chain Monte Carlo strategy. To evaluate the efficiency, a local non-degeneracy is introduced, and we also provide a numerical simulation to show the effect.

Journal

• JOURNAL OF THE JAPAN STATISTICAL SOCIETY

  JOURNAL OF THE JAPAN STATISTICAL SOCIETY 44 (1), 1-23, 2014

  THE JAPAN STATISTICAL SOCIETY

Keywords

• Bayesian inference
• Markov chain
• Monte Carlo method
• probit model

Details

• CRID: 1390282680264898816
• NII Article ID: 130004951124
• NII Book ID: AA1105098X
• DOI: 10.14490/jjss.44.1
• ISSN: 13486365
• ISSN: 18822754
• NDL BIB ID: 025821372
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I025821372
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed