Distributed Simultaneous Inference in Generalized Linear Models via Confidence Distribution.

ABSTRACT: We propose a distributed method for simultaneous inference for datasets with sample size much larger than the number of covariates, i.e., N ≫ p, in the generalized linear models framework. When such datasets are too big to be analyzed entirely by a single centralized computer, or when datasets are already stored in distributed database systems, the strategy of divide-and-combine has been the method of choice for scalability. Due to partition, the sub-dataset sample sizes may be uneven and some possibly close to p, which calls for regularization techniques to improve numerical stability. However, there is a lack of clear theoretical justification and practical guidelines to combine results obtained from separate regularized estimators, especially when the final objectiv

SUBMITTER: Tang L 

PROVIDER: S-EPMC7453826 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature