The least absolute deviation (LAD) regression is a useful method for robust regression, and the least absolute shrinkage and selection operator (lasso) is a popular choice for shrinkage estimation and variable selection. In this article we combine these two classical ideas together to produce...

We consider here a large-scale social network with a continuous response observed for each node at equally spaced time points. The responses from different nodes constitute an ultra-high dimensional vector, whose time series dynamic is to be investigated. In addition, the network structure is...

In social network analysis, many estimation methods have been developed over the past three decades. Due to the computational complexity for analyzing large-scale social network data, however, those methods cannot be applied effectively. On the other hand, the structure of large-scale network...

The mean-variance theory of Markowitz (1952) indicates that large investment portfolios naturally provide better risk diversi cation than small ones. However, due to parameter estimation errors, one may find ambiguous results in practice. Hence, it is essential to identify relevant stocks to...

In finite mixture regression models, we generalize the application of the least absolute shrinkage and selection operator (LASSO) to obtain MR-Lasso, which incorporates both mixture and regression penalties. Because MR-Lasso jointly penalizes both regression coeficients and mixture components,...