While large training datasets generally offer improvement in model performance, the training
process becomes computationally expensive and time consuming. Distributed learning is a …

We study distributed learning of nonparametric conditional quantiles with Tikhonov
regularization in a reproducing kernel Hilbert space (RKHS). Although distributed parametric …

Smoothing splines have been used pervasively in nonparametric regressions. However, the
computational burden of smoothing splines is significant when the sample size n is large …

This paper studies the estimation of Gaussian graphical models in the unbalanced
distributed framework. It provides an effective approach when the available machines are of …

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 …

Abstract Coronavirus 2019 (COVID‐19) has caused violent fluctuation in stock markets, and
led to heated discussion in stock forums. The rise and fall of any specific stock is influenced …

The nonparametric additive model (NAM) is a widely used nonparametric regression
method. Nevertheless, due to the high computational burden, classic statistical techniques …

The accelerated failure time (AFT) model is an appealing tool in survival analysis because of
its ease of interpretation, but when there is a large volume of data, fitting an AFT model and …

We propose a new functional additive hazards model to investigate the potential effects of
functional and scalar predictors on mortality risks, and develop a penalized least squares …

With the development of science and technology, massive datasets stored in multiple
machines are increasingly prevalent. It is known that traditional statistical methods may be …