Comments on “A Gibbs Sampler for a Class of Random Convex Polytopes”
Comments on “A Gibbs Sampler for a Class of Random Convex Polytopes”
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对“一类随机凸多面体的吉布斯采样器”的评论
DOI:
10.1080/01621459.2021.1950002
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发表时间:
2021
影响因子:
3.7
通讯作者:
Zhang, Kai
中科院分区:
文献类型:
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作者:
Hoffman, Kentaro;Hannig, Jan;Zhang, Kai
We would like to congratulate the authors for their contribution to this longstanding open problem in mathematical statistics. Their clever implementation of MCMC to obtain simplex-based Dempster–Shafer (DS) samples for parameters of multinomial distribution and its connection to graph theory is extremely thought provoking. We expect that the contributions seen in this article will have impacts for years to come. In our comment, we would like to contribute our thoughts particularly on the applications of the various DS approaches to nonparametric tests of independence. Nonparametric dependence detection is a classical statistical problem but recently gains great interest from both statisticians and computer scientists due to its applications in machine learning. See Hoeffding (1948), Székely, Rizzo, and Bakirov (2007), Gretton et al.(2007), Reshef et al.(2011), Heller, Heller, and Gorfine (2013), Chatterjee (2020), Shi et al.(2020), and references therein.One general approach in nonparametric tests of independence is the multiresolution approach. See some recent works by Ma and Mao (2019), Zhang (2019), Lee, Zhang, and Kosorok (2019), Gorsky and Ma (2018), and Zhang, Zhao, and Zhou (2021). Some advantages of this approach include uniform consistency, minimax optimal power, clear interpretability and efficient computation.
DOI:
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发表时间:
2020
期刊:
影响因子:
--
作者:
Hongjian Shi;M. Hallin;M. Drton;Fang Han
通讯作者:
Fang Han
DOI:
10.1080/01621459.2020.1758115
发表时间:
2020-05-28
影响因子:
3.7
作者:
Chatterjee, Sourav
通讯作者:
Chatterjee, Sourav