.png){kind=icon}
.png){kind=icon}
Return
Communication-Efficient Distributed Estimation and Computation Using Skew-Normal Distribution
.png){kind=icon}
PRE
AI
DOI：10.1007/s10114-025-3520-z.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we introduce a communication-efficient distributed estimation method tailored for massive datasets exhibiting skewness. The data are stored across multiple machines. We construct a surrogate likelihood which only need to transfer subgradient from local machines to approximate higher-order derivatives of the global likelihood. An enhanced EM algorithm is developed for computations. The proposed method not only addresses the non-normality of data by utilizing first-order gradient information in each transmission, ensuring low communication overhead, but also ensures privacy protection. Simulation studies illustrate the superior performance of the proposed methods.
Keywords:
Skew-normal distribution
massive data
distributed estimation
Journal
A
IF:
0.9
Papers:
92
Citations:
0