CORN：相关性驱动的非参数投资组合学习方法--在线附录
CORN: Correlation-Driven Nonparametric Learning Approach for Portfolio Selection -- an Online Appendix
摘要：
这个附录证明了CORN的普遍一致性。Bin的一位博士论文审查员（特别感谢来自伦敦大学皇家霍洛威学院的Vladimir Vovk）提出了CORN是普适的，并提供了引理1.6的草图证明，这是这个证明的关键。基于Gyprfi等人[2006]的证明，我们证明了CORN的泛相容性。请注意，本附录中的符号遵循Györfi et al. [2006]。
英文摘要：
This appendix proves CORN's universal consistency. One of Bin's PhD thesis examiner (Special thanks to Vladimir Vovk from Royal Holloway, University of London) suggested that CORN is universal and provided sketch proof of Lemma 1.6, which is the key of this proof. Based on the proof in Gyprfi et al. [2006], we thus prove CORN's universal consistency. Note that the notations in this appendix follows Györfi et al. [2006].
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
