Tutorial: Minimizing robust density power-based divergences for general parametric density models
##article.authors##
-
Akifumi Okuno
Institute of Statistical Mathematics, Department of Statistical Inference and Mathematics
https://researchmap.jp/aokuno
DOI:
https://doi.org/10.51094/jxiv.642Keywords:
Density power divergence, β-divergence, Stochastic optimizationAbstract
本稿は雑誌 Annals of the Institute of Statistical Mathematics に採択された，冪密度ダイバージェンスの最小化に関する我々の論文: Okuno (2024) の解説です．解説の平易さを優先するため，より厳密な記述については原著論文をご参照ください．
Conflicts of Interest Disclosure
No conflict of interest.Downloads *Displays the aggregated results up to the previous day.
References
Basu, A., Harris, I. R., Hjort, N. L., and Jones, M. C. (1998). Robust and efficient estimation by minimising a density power divergence. Biometrika, 85(3):549-559.
Lan, G. (2020). First-order and Stochastic Optimization Methods for Machine Learning. Springer Series in the Data Sciences. Springer International Publishing.
Okuno, A. (2024). Minimizing robust density power-based divergences for general parametric density models. Annals of the Institute of Statistical Mathematics. to appear.
Robbins, H. and Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3):400-407.
Downloads
Posted
Submitted: 2024-03-18 02:31:01 UTC
Published: 2024-03-19 00:31:25 UTC
License
Copyright (c) 2024
Akifumi Okuno
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
