6 citations · 10 across the 5 of their papers we have counts for
5 papers
Signal Lasso with Non-Convex Penalties for Efficient Network Reconstruction and Topology Inference
Lei Shi, Jie Hu, Huaiyu Tan +3
Inferring network structures remains an interesting question for its importance on the understanding and controlling collective dynamics of complex systems. The existing shrinking…
Robust and Efficient Network Reconstruction in Complex System via Adaptive Signal Lasso
Lei Shi, Jie Hu, Libin Jin +3
Network reconstruction is important to the understanding and control of collective dynamics in complex systems. Most real networks exhibit sparsely connected properties, and the co…
Inferring Network Structures via Signal Lasso
Lei Shi, Chen Shen, Libin Jin +4
Inferring the connectivity structure of networked systems from data is an extremely important task in many areas of science. Most of real-world networks exhibit sparsely connected…
Quantile Regression for Partially Linear Varying Coefficient Spatial Autoregressive Models
Xiaowen Dai, Shaoyang Li, Maozai Tian
This paper considers the quantile regression approach for partially linear spatial autoregressive models with possibly varying coefficients. B-spline is employed for the approximat…
Penalized Maximum Likelihood Estimator for Skew Normal Mixtures
Libin Jin, Wangli Xu, Liping Zhu +1
Skew normal mixture models provide a more flexible framework than the popular normal mixtures for modelling heterogeneous data with asymmetric behaviors. Due to the unboundedness o…