Mohammed, T., Joe-Wong, C., Babbar, R. and Di Francesco, M. (2020) Distributed Inference Acceleration with Adaptive DNN Partitioning and Offloading. IEEE INFOCOM 2020-IEEE Conference on Computer Communications, Toronto, 6-9 July 2020, 854-863.
https://doi.org/10.1109/INFOCOM41043.2020.9155237

has been cited by the following article:

• TITLE: Distributed Estimator of Market Beta under Extreme Conditions

  AUTHORS: Suyu Zhu

  KEYWORDS: Heavy Tail, Tail Dependence, Distributed Statistical Inference, Market Beta

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.11 No.11, November 28, 2023

  ABSTRACT: Market beta is a measure of the volatility or systematic risk of a security or portfolio compared to the market as a whole. This paper considers the distributed estimation of market beta in the case of massive data, and obtains the consistency and asymptotic normality of the estimator. Further, simulations show the finite sample properties of this estimator.