On MDL Estimation for Simple Contaminated Gaussian Location Families

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

The performance of MDL density estimators defined as the minimizer of two part code lengths isguaranteed in terms of the redundancy of the two part code [2], [3]. When the true density belongs to the assumed model, the redundancy of a code can be bounded by the regret (pointwise redundancy) of the code. Then, the construction of two part codes which achieve small regret based on quantization of parametric family is developed. For exponential families, it is known that we can achieve sufficiently small regret by using this construction [4]. For non-exponential families, the evaluation of the regret achieved by using this construction breaks. However, for non-exponential families under certain assumptions, by enhancing this construction using local exponentially family bundles [1], we can design efficient two part codes [9]. In this paper, we show that we can apply this coding method to contamination model [5] with simple settings and give the guarantee of performance of MDL estimators for them.

本文言語英語
ホスト出版物のタイトルProceedings of 2020 International Symposium on Information Theory and its Applications, ISITA 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ587-591
ページ数5
ISBN(電子版)9784885523304
出版ステータス出版済み - 10 24 2020
イベント16th International Symposium on Information Theory and its Applications, ISITA 2020 - Virtual, Kapolei, 米国
継続期間: 10 24 202010 27 2020

出版物シリーズ

名前Proceedings of 2020 International Symposium on Information Theory and its Applications, ISITA 2020

会議

会議16th International Symposium on Information Theory and its Applications, ISITA 2020
Country米国
CityVirtual, Kapolei
Period10/24/2010/27/20

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems
  • Software
  • Theoretical Computer Science

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