Flight delays are a significant problem for society as they evenly impair airlines, transport
companies, air traffic controllers, facility managers, and passengers. Studying prior flight …

Bayesian models provide recursive inference naturally because they can formally reconcile
new data and existing scientific information. However, popular use of Bayesian methods …

In the context of big data analysis, the divide-and-conquer methodology refers to a multiple-
step process: first splitting a data set into several smaller ones; then analyzing each set …

Online updating is an important statistical method for the analysis of big data arriving in
streams due to its ability to break the storage barrier and the computational barrier under …

With the development of modern technologies, it is possible to gather an extraordinarily
large number of observations. Due to the storage or transmission burden, big data are …

When large amounts of survival data arrive in streams, conventional estimation methods
become computationally infeasible since they require access to all observations at each …

Divide and conquer algorithm is a common strategy applied in big data. Model averaging
has the natural divide-and-conquer feature, but its theory has not been developed in big …

With increasing availability of massive survival data, researchers need valid statistical
inferences for survival modeling whose computation is not limited by computer memories …

For massive data, subsampling techniques are popular to mitigate computational burden by
reducing the data size. In a subsampling approach, subsampling probabilities for each data …

In this article, we tackle the estimation and inference problem of analyzing distributed
streaming data that is collected continuously over multiple data sites. We propose an online …